Fast training of accurate physics-informed neural networks without gradient descent

ICLROral2026

Authors
Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak, Anna Veselovska, Massimo Fornasier, Felix Dietrich
Affiliation
Technische Universität München
Venue
ICLR 2026
Track
Oral

TL;DR

Our approach - Frozen-PINNs addresses longstanding training and accuracy bottlenecks of Physics-Informed Neural Networks (PINNs) and makes PINNs highly realize high-precision, temporal causality, and extremely fast training.

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Topics

causality physics causal

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