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#machine learning Preprint Sep 2026

CI-PINN: Causal Integral Physics-Informed Neural Network for Solving Evolution Equations

Physics-informed neural networks (PINNs) solve partial differential equations (PDEs) by incorporating governing physical laws into the training loss. For evolution equations, however, their conventional pointwise space--time representation does not explicitly encode temporal dependence, which can hinder accurate predic...

Xiao-Dong Feng, Zi-Yue Sun, Tao Tang et al. · 0 citations
#machine learning Preprint Apr 2026

Amortized Filtering and Smoothing with Conditional Normalizing Flows

This work proposes an amortized framework for filtering and smoothing that reuses trained conditional models across observation sequences and assimilation times and shows that, under the Markov assumption, a summary sufficient for filtering is also sufficient for the backward kernel.

Tiangang Cui, Xiao-Dong Feng, Chenlong Pei et al. · 2 citations

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