Author

Jia-Chen Yao

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#machine learning Preprint Oct 2026

Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics

Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical solvers or autoregressive models that advance one small step at a time, which makes long horizons expensive. We instead view this problem as learning the system's time-inv...

Lu-Fang Chiang, Jia-Chen Yao, Thomas Y. L. Lin et al. · 0 citations

#machine learning Preprint Sep 2026

PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers

Generative models are increasingly used to solve scientific inverse problems, but existing evaluations still focus primarily on whether a method can produce a single plausible reconstruction. This is insufficient for ill-posed problems, where multiple solutions may be consistent with the same sparse or noisy observatio...

Jia-Chen Yao, Zi-Siang Hsu, Xi Deng et al. · 0 citations

#artificial intelligence May 2025

EquiReg: Equivariance Regularized Diffusion for Inverse Problems

EquiReg formalizes manifold-preferential equivariant functions that exhibit low equivariance error for on-manifold samples and high error for off-manifold ones, thereby guiding sampling toward symmetry-preserving regions of the solution space.

Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi et al. · arXiv.org · 4 citations