MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
- URL: http://arxiv.org/abs/2303.03181v1
- Date: Mon, 6 Mar 2023 14:48:30 GMT
- Title: MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
- Authors: S Chandra Mouli, Muhammad Ashraful Alam, Bruno Ribeiro
- Abstract summary: A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks.
We propose a solution for such tasks, which we define as a meta-learning procedure for causal structure discovery.
Using three different OOD tasks, we empirically observe that the proposed approach significantly outperforms existing state-of-the-art PIML and deep learning methods.
- Score: 15.657161498824738
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A fundamental challenge in physics-informed machine learning (PIML) is the
design of robust PIML methods for out-of-distribution (OOD) forecasting tasks.
These OOD tasks require learning-to-learn from observations of the same (ODE)
dynamical system with different unknown ODE parameters, and demand accurate
forecasts even under out-of-support initial conditions and out-of-support ODE
parameters. In this work we propose a solution for such tasks, which we define
as a meta-learning procedure for causal structure discovery (including
invariant risk minimization). Using three different OOD tasks, we empirically
observe that the proposed approach significantly outperforms existing
state-of-the-art PIML and deep learning methods.
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