arXiv Machine Learning By Tianyue Yang, Xiao Xue

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

Read the original on arXiv Machine Learning →

arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 2

TRIE: An Evaluation Framework for Stochastic PDE Surrogates

arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.

By Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar, Charles D. Young
arXiv Machine Learning
Aug 7

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.

By Guillaume Couairon, Alexis Jacq, Yu-Han Wu, Renu Singh, Yana Hasson, Quentin Berthet, Romuald Elie