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• Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
  2022 · 160 citations
• Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
  2022 · 24 citations
• Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
  2023 · 21 citations
• NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
  2022 · 13 citations
• Correcting model misspecification in physics-informed neural networks (PINNs)
  2023 · 10 citations
• Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators
  2023 · 6 citations
• Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions
  2023 · 5 citations
• Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems
  2023 · 5 citations
• Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems
  2026
• Self-supervised neural operator for solving partial differential equations
  2025