Scientific machine learning through physics–informed neural networks: Where we are and what's next
Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …
model equations, like Partial Differential Equations (PDE), as a component of the neural …
Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Partial differential equations (PDEs) play a central role in the mathematical analysis and
modeling of complex dynamic processes across all corners of science and engineering …
modeling of complex dynamic processes across all corners of science and engineering …
Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
Physics-informed neural networks with hard constraints for inverse design
Inverse design arises in a variety of areas in engineering such as acoustic, mechanics,
thermal/electronic transport, electromagnetism, and optics. Topology optimization is an …
thermal/electronic transport, electromagnetism, and optics. Topology optimization is an …
When and why PINNs fail to train: A neural tangent kernel perspective
Physics-informed neural networks (PINNs) have lately received great attention thanks to
their flexibility in tackling a wide range of forward and inverse problems involving partial …
their flexibility in tackling a wide range of forward and inverse problems involving partial …
Physics‐informed neural networks (PINNs) for wave propagation and full waveform inversions
We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …
Understanding and mitigating gradient flow pathologies in physics-informed neural networks
The widespread use of neural networks across different scientific domains often involves
constraining them to satisfy certain symmetries, conservation laws, or other domain …
constraining them to satisfy certain symmetries, conservation laws, or other domain …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great
potential in the breakthrough of metal additive manufacturing (AM) process modeling, which …
potential in the breakthrough of metal additive manufacturing (AM) process modeling, which …