arXiv AI July 28, 2026 By Akila Sampath, Vandana P. Janeja, Jianwu Wang

Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation

arXiv:2601. 17074v5 Announce Type: replace-cross Abstract: Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

computer-vision

arXiv AI

Jun 2

Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

arXiv:2601. 17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains.

By Akila Sampath, Vandana Janeja, Jianwu Wang

computer-vision

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv Machine Learning

Sep 22

Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation

arXiv:2609.24882v1 Announce Type: new Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hi...

By Jurij Sch\"onfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring

diffusion efficiency

arXiv AI

Jun 16

Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction

arXiv:2606. 15288v1 Announce Type: cross Abstract: Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models.

By Yiquan Gao, Duohui Xu

llms

arXiv Machine Learning

Aug 11

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.

By Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi

fine-tuning

arXiv Machine Learning

Aug 11

NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements

arXiv:2507. 03094v2 Announce Type: replace-cross Abstract: Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators.

By Ali SaraerToosi, Renbo Tu, Esther Y. H. Lin, Kamyar Azizzadenesheli, Aviad Levis

efficiency

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Related stories

Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

Physics-Informed Machine Learning for Short-Term Flood Prediction

Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation

Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements