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A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks.
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Physics-guided neural networks (pgnn): An application in lake temperature modeling
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Bilevel programming for hyperparameter optimization and meta-learning
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Multi-domain causal structure learning in linear systems
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Causal structure learning
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Generalized Score Functions for Causal Discovery
Huang, B., Zhang, K., Lin, Y., Schölkopf, B., and Glymour, C · 2018
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Systems biology informed deep learning for inferring parameters and hidden dynamics
Yazdani, A., Lu, L., Raissi, M., and Karniadakis, G. E · 2020
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Learning Symbolic Expressions via Gumbel-Max Equation Learner Networks
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Deep learning for universal linear embeddings of nonlinear dynamics
Lusch, B., Kutz, J. N., and Brunton, S. L · 2018
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Deep hidden physics models: Deep learning of nonlinear partial differential equations
Raissi, M · 2018
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Multistep neural networks for data-driven discovery of nonlinear dynamical systems
Raissi, M., Perdikaris, P., and Karniadakis, G. E · 2018
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Deep learning algorithm for data-driven simulation of noisy dynamical system
Yeo, K. and Melnyk, I · 2018
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Dags with no tears: Continuous optimization for structure learning
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Out-of-distribution generalization via risk extrapolation (rex)
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Neural dynamical systems: Balancing structure and flexibility in physical prediction
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How neural networks extrapolate: From feedforward to graph neural networks
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Augmenting physical models with deep networks for complex dynamics forecasting
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Tnt: Vision transformer for turbulence simulations
Dang, Y., Hu, Z., Cranmer, M., Eickenberg, M., and Ho, S · 2022
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Generalizing to New Physical Systems via Context-Informed Dynamics Model
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Masked gradient-based causal structure learning
Ng, I., Zhu, S., Fang, Z., Li, H., Chen, Z., and Wang, J · 2022
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Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis
Perry, R., von Kügelgen, J., and Schölkopf, B · 2022
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Causal models for dynamical systems
Peters, J., Bauer, S., and Pfister, N · 2022
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Respecting causality is all you need for training physics-informed neural networks
Wang, S., Sankaran, S., and Perdikaris, P · 2022
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Gradient-enhanced physics-informed neural networks for forward and inverse pde problems
Yu, J., Lu, L., Meng, X., and Karniadakis, G. E · 2022
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