Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method

Deep learning of subsurface flow via theory-guided neural network

(2020) Nanzhe Wang et al.   JOURNAL OF HYDROLOGY

Adversarial uncertainty quantification in physics-informed neural networks

(2019) Yibo Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

(2019) Dongkun Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

(2019) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING