Iryna Burak — accepted AI-conference papers

2026

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

Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak +3

ICLR 2026oral

Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their accuracy and training speed are limited by two core barriers:…

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2023

Sampling weights of deep neural networks

Erik Lien Bolager, Iryna Burak, Chinmay Datar, Qing Sun, Felix Dietrich

NeurIPS 2023poster

We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a supervised learning context, no iterative optimization or gradient computations of internal network parameters are needed to obtain a trained networ…

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