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• Respecting causality is all you need for training physics-informed neural networks
  2022 · 118 citations
• An Expert's Guide to Training Physics-informed Neural Networks
  2023 · 66 citations
• Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
  2022 · 35 citations
• A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
  2019 · 34 citations
• NOMAD: Nonlinear Manifold Decoders for Operator Learning
  2022 · 29 citations
• PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
  2023 · 23 citations
• Long-time integration of parametric evolution equations with physics-informed DeepONets
  2021 · 9 citations
• Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
  2025 · 8 citations
• Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
  2022 · 4 citations
• Deep Learning Alternatives of the Kolmogorov Superposition Theorem
  2024 · 2 citations
• Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
  2025 · 1 citations
• CViT: Continuous Vision Transformer for Operator Learning
  2024 · 1 citations
• $Δ$-PINNs: physics-informed neural networks on complex geometries
  2022 · 1 citations
• Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics
  2025