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Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations

2 May 2024arXiv:2405.01680archive 2025-07-28

Nima Hosseini Dashtbayaz, Ghazal Farhani, Boyu Wang, Charles X. Ling

The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this work, we analyze the residual loss by studying its characteristics at critical points to find the conditions that result in effective training of PINNs. Specifically, we first show that under certain conditions, the residual loss of PINNs can be globally minimized by a wide neural network. Furthermore, our analysis also reveals that an activation function with well-behaved high-order derivatives plays a crucial role in minimizing the residual loss. In particular, to solve a k-th order PDE, the k-th derivative of the activation function should be bijective. The established theory paves the way for designing and choosing effective activation functions for PINNs and explains why periodic activations have shown promising performance in certain cases. Finally, we verify our findings by conducting a set of experiments on several PDEs. Our code is publicly available at https://github.com/nimahsn/pinns_tf2.

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create_dense_model nimahsn/pinns_tf2/modules/models.py official repository ran MIT (permissive) · 99208346b28d260e · report
get_train_plot_name nimahsn/pinns_tf2/modules/utils.py official repository ran MIT (permissive) · d05f6cd21794edca · report
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plot_wave_model_slider nimahsn/pinns_tf2/modules/plots.py official repository ran MIT (permissive) · 4f54163d268a0e14 · report
simulate_burgers nimahsn/pinns_tf2/modules/data.py official repository ran MIT (permissive) · 35ff247081adb369 · report
simulate_heat nimahsn/pinns_tf2/modules/data.py official repository ran MIT (permissive) · d4c418fa17abf950 · report
simulate_wave nimahsn/pinns_tf2/modules/data.py official repository ran MIT (permissive) · f0d83de3292aa3f2 · report
get_id nimahsn/pinns_tf2/modules/utils.py official repository unverified MIT (permissive) · c63585033cb0f861 · report

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