PINF: Continuous Normalizing Flows for Physics-Constrained Deep Learning
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Abstract (arXiv)
The normalization constraint on probability density poses a significant challenge for solving the Fokker-Planck equation. Normalizing Flow, an invertible generative model leverages the change of variables formula to ensure probability density conservation and enable the learning of complex data distributions. In this paper, we introduce Physics-Informed Normalizing Flows (PINF), a novel extension of continuous normalizing flows, incorporating diffusion through the method of characteristics. Our method, which is mesh-free and causality-free, can efficiently solve high dimensional time-dependent and steady-state Fokker-Planck equations.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 35 references that are in the map. First posted on arXiv 26 Sep 2023.
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21 of 35 references.
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9 more
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