arXiv Machine Learning July 2, 2026 By Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar, Charles D. Young

TRIE: An Evaluation Framework for Stochastic PDE Surrogates

Read the original on arXiv Machine Learning →

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.

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

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Related stories

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

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

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