Physics-Informed Deep Equilibrium Models. This means applying the regularization proposed by Raissi et al. (2019) to the equilibrium models as described by Ba et al. (2019). PIDEQs were the theme of my Baschelor's thesis, which is available here.

PIDEQ is tested on the Van der Pol oscillator. All experiments were tracked using W&B: https://wandb.ai/brunompac/pideq-vdp.

Raissi, M., P. Perdikaris, and G. E. Karniadakis. “Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations.” Journal of Computational Physics 378 (February 1, 2019): 686–707. https://doi.org/10.1016/J.JCP.2018.10.045.

Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. “Deep Equilibrium Models.” Advances in Neural Information Processing Systems 32 (September 3, 2019). https://doi.org/10.48550/arxiv.1909.01377.