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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11194">arXiv:2407.11194</a> <span> [<a href="https://arxiv.org/pdf/2407.11194">pdf</a>, <a href="https://arxiv.org/format/2407.11194">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> AstroMLab 1: Who Wins Astronomy Jeopardy!? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ting%2C+Y">Yuan-Sen Ting</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T+D">Tuan Dung Nguyen</a>, <a href="/search/cs?searchtype=author&query=Ghosal%2C+T">Tirthankar Ghosal</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+R">Rui Pan</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+H">Hardik Arora</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zechang Sun</a>, <a href="/search/cs?searchtype=author&query=de+Haan%2C+T">Tijmen de Haan</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Wells%2C+A">Azton Wells</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">Sandeep Madireddy</a>, <a href="/search/cs?searchtype=author&query=Accomazzi%2C+A">Alberto Accomazzi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.11194v2-abstract-short" style="display: inline;"> We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and asse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11194v2-abstract-full').style.display = 'inline'; document.getElementById('2407.11194v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11194v2-abstract-full" style="display: none;"> We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11194v2-abstract-full').style.display = 'none'; document.getElementById('2407.11194v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages, 12 figures, 7 tables. Published in Astronomy & Computing. AstroMLab homepage: https://astromlab.org/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12528">arXiv:2310.12528</a> <span> [<a href="https://arxiv.org/pdf/2310.12528">pdf</a>, <a href="https://arxiv.org/format/2310.12528">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huppenkothen%2C+D">D. Huppenkothen</a>, <a href="/search/cs?searchtype=author&query=Ntampaka%2C+M">M. Ntampaka</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+M">M. Ho</a>, <a href="/search/cs?searchtype=author&query=Fouesneau%2C+M">M. Fouesneau</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</a>, <a href="/search/cs?searchtype=author&query=Peek%2C+J+E+G">J. E. G. Peek</a>, <a href="/search/cs?searchtype=author&query=Walmsley%2C+M">M. Walmsley</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+J+F">J. F. Wu</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">C. Avestruz</a>, <a href="/search/cs?searchtype=author&query=Buck%2C+T">T. Buck</a>, <a href="/search/cs?searchtype=author&query=Brescia%2C+M">M. Brescia</a>, <a href="/search/cs?searchtype=author&query=Finkbeiner%2C+D+P">D. P. Finkbeiner</a>, <a href="/search/cs?searchtype=author&query=Goulding%2C+A+D">A. D. Goulding</a>, <a href="/search/cs?searchtype=author&query=Kacprzak%2C+T">T. Kacprzak</a>, <a href="/search/cs?searchtype=author&query=Melchior%2C+P">P. Melchior</a>, <a href="/search/cs?searchtype=author&query=Pasquato%2C+M">M. Pasquato</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">N. Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Ting%2C+Y+-">Y. -S. Ting</a>, <a href="/search/cs?searchtype=author&query=van+de+Ven%2C+G">G. van de Ven</a>, <a href="/search/cs?searchtype=author&query=Villar%2C+S">S. Villar</a>, <a href="/search/cs?searchtype=author&query=Villar%2C+V+A">V. A. Villar</a>, <a href="/search/cs?searchtype=author&query=Zinger%2C+E">E. Zinger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.12528v1-abstract-short" style="display: inline;"> Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12528v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12528v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12528v1-abstract-full" style="display: none;"> Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12528v1-abstract-full').style.display = 'none'; document.getElementById('2310.12528v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.16869">arXiv:2303.16869</a> <span> [<a href="https://arxiv.org/pdf/2303.16869">pdf</a>, <a href="https://arxiv.org/format/2303.16869">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luan%2C+L">Lele Luan</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Ravi%2C+S+K">Sandipp Krishnan Ravi</a>, <a href="/search/cs?searchtype=author&query=Bhaduri%2C+A">Anindya Bhaduri</a>, <a href="/search/cs?searchtype=author&query=Pandita%2C+P">Piyush Pandita</a>, <a href="/search/cs?searchtype=author&query=Balaprakash%2C+P">Prasanna Balaprakash</a>, <a href="/search/cs?searchtype=author&query=Anitescu%2C+M">Mihai Anitescu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+C">Changjie Sun</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+L">Liping Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.16869v2-abstract-short" style="display: inline;"> Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization. The higher fidelity in these computer models makes it computationally intensive to query them hundreds of times for optimization and one usually relies on a simplified model albeit at the cost of l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16869v2-abstract-full').style.display = 'inline'; document.getElementById('2303.16869v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.16869v2-abstract-full" style="display: none;"> Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization. The higher fidelity in these computer models makes it computationally intensive to query them hundreds of times for optimization and one usually relies on a simplified model albeit at the cost of losing predictive accuracy and precision. Towards this, data-driven surrogate modeling methods have shown a lot of promise in emulating the behavior of the expensive computer models. However, a major bottleneck in such methods is the inability to deal with high input dimensionality and the need for relatively large datasets. With such problems, the input and output quantity of interest are tensors of high dimensionality. Commonly used surrogate modeling methods for such problems, suffer from requirements like high number of computational evaluations that precludes one from performing other numerical tasks like uncertainty quantification and statistical analysis. In this work, we propose an end-to-end approach that maps a high-dimensional image like input to an output of high dimensionality or its key statistics. Our approach uses two main framework that perform three steps: a) reduce the input and output from a high-dimensional space to a reduced or low-dimensional space, b) model the input-output relationship in the low-dimensional space, and c) enable the incorporation of domain-specific physical constraints as masks. In order to accomplish the task of reducing input dimensionality we leverage principal component analysis, that is coupled with two surrogate modeling methods namely: a) Bayesian hybrid modeling, and b) DeepHyper's deep neural networks. We demonstrate the applicability of the approach on a problem of a linear elastic stress field data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16869v2-abstract-full').style.display = 'none'; document.getElementById('2303.16869v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 16 figures, IDETC Conference Submission</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.03284">arXiv:2208.03284</a> <span> [<a href="https://arxiv.org/pdf/2208.03284">pdf</a>, <a href="https://arxiv.org/ps/2208.03284">ps</a>, <a href="https://arxiv.org/format/2208.03284">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.2172/1886020">10.2172/1886020 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interpretable Uncertainty Quantification in AI for HEP </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+T+Y">Thomas Y. Chen</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+B">Biprateep Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+A">Aishik Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kagan%2C+M">Michael Kagan</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.03284v3-abstract-short" style="display: inline;"> Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03284v3-abstract-full').style.display = 'inline'; document.getElementById('2208.03284v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.03284v3-abstract-full" style="display: none;"> Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03284v3-abstract-full').style.display = 'none'; document.getElementById('2208.03284v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-FN-1179-SCD; arXiv:2208.03284 oai:inspirehep.net:2132723 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.00554">arXiv:2101.00554</a> <span> [<a href="https://arxiv.org/pdf/2101.00554">pdf</a>, <a href="https://arxiv.org/format/2101.00554">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s42256-021-00402-2">10.1038/s42256-021-00402-2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fukami%2C+K">Kai Fukami</a>, <a href="/search/cs?searchtype=author&query=Maulik%2C+R">Romit Maulik</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Fukagata%2C+K">Koji Fukagata</a>, <a href="/search/cs?searchtype=author&query=Taira%2C+K">Kunihiko Taira</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.00554v2-abstract-short" style="display: inline;"> Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Moreover, these sensors can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.00554v2-abstract-full').style.display = 'inline'; document.getElementById('2101.00554v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.00554v2-abstract-full" style="display: none;"> Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Moreover, these sensors can be in motion and can become online or offline over time. The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors. As a solution to this problem, we propose a data-driven spatial field recovery technique founded on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. It should be noted that the na茂ve use of machine learning becomes prohibitively expensive for global field reconstruction and is furthermore not adaptable to an arbitrary number of sensors. In the present work, we consider the use of Voronoi tessellation to obtain a structured-grid representation from sensor locations enabling the computationally tractable use of convolutional neural networks. One of the central features of the present method is its compatibility with deep-learning based super-resolution reconstruction techniques for structured sensor data that are established for image processing. The proposed reconstruction technique is demonstrated for unsteady wake flow, geophysical data, and three-dimensional turbulence. The current framework is able to handle an arbitrary number of moving sensors, and thereby overcomes a major limitation with existing reconstruction methods. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.00554v2-abstract-full').style.display = 'none'; document.getElementById('2101.00554v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.12167">arXiv:2007.12167</a> <span> [<a href="https://arxiv.org/pdf/2007.12167">pdf</a>, <a href="https://arxiv.org/format/2007.12167">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.physd.2020.132797">10.1016/j.physd.2020.132797 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Maulik%2C+R">Romit Maulik</a>, <a href="/search/cs?searchtype=author&query=Botsas%2C+T">Themistoklis Botsas</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Mason%2C+L+R">Lachlan Robert Mason</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+I">Indranil Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.12167v2-abstract-short" style="display: inline;"> Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.12167v2-abstract-full').style.display = 'inline'; document.getElementById('2007.12167v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.12167v2-abstract-full" style="display: none;"> Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.12167v2-abstract-full').style.display = 'none'; document.getElementById('2007.12167v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.03867">arXiv:1911.03867</a> <span> [<a href="https://arxiv.org/pdf/1911.03867">pdf</a>, <a href="https://arxiv.org/format/1911.03867">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">Sandeep Madireddy</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Li%2C+N">Nan Li</a>, <a href="/search/cs?searchtype=author&query=Butler%2C+J">James Butler</a>, <a href="/search/cs?searchtype=author&query=Balaprakash%2C+P">Prasanna Balaprakash</a>, <a href="/search/cs?searchtype=author&query=Habib%2C+S">Salman Habib</a>, <a href="/search/cs?searchtype=author&query=Heitmann%2C+K">Katrin Heitmann</a>, <a href="/search/cs?searchtype=author&query=Collaboration%2C+T+L+D+E+S">The LSST Dark Energy Science Collaboration</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.03867v3-abstract-short" style="display: inline;"> Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale image distortions. The absence of large quantities of representative data from current astronomical surveys motivates the development of a robust forward-modeli… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.03867v3-abstract-full').style.display = 'inline'; document.getElementById('1911.03867v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.03867v3-abstract-full" style="display: none;"> Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale image distortions. The absence of large quantities of representative data from current astronomical surveys motivates the development of a robust forward-modeling approach using synthetic lensing images. Using a mock sample of strong lenses created upon a state-of-the-art extragalactic catalogs, we train a modular deep learning pipeline for uncertainty-quantified detection and modeling with intermediate image processing components for denoising and deblending the lensing systems. We demonstrate a high degree of interpretability and controlled systematics due to domain-specific task modules trained with different stages of synthetic image generation. For lens detection and modeling, we obtain semantically meaningful latent spaces that separate classes of strong lens images and yield uncertainty estimates that explain the origin of misclassified images and provide probabilistic predictions for the lens parameters. Validation of the inference pipeline has been carried out using images from the Subaru telescope's Hyper Suprime-Cam camera, and LSST DESC simulated DC2 sky survey catalogues. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.03867v3-abstract-full').style.display = 'none'; document.getElementById('1911.03867v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" 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