5 papers
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
Alexander Heinlein, Taniya Kapoor
Approximating the solutions of boundary value problems governed by partial differential equations with neural networks is challenging, largely due to the difficult training process…
Resolving Extreme Data Scarcity by Explicit Physics Integration: An Application to Groundwater Heat Transport
Julia Pelzer, Corné Verburg, Alexander Heinlein +1
Real-world flow applications in complex scientific and engineering domains, such as geosciences, challenge classical simulation methods due to large spatial domains, high spatio-te…
Numerical study on hyper parameter settings for neural network approximation to partial differential equations
Hee Jun Yang, Alexander Heinlein, Hyea Hyun Kim
Approximate solutions of partial differential equations (PDEs) obtained by neural networks are highly affected by hyper parameter settings. For instance, the model training strongl…
Overlapping Schwarz Preconditioners for Randomized Neural Networks with Domain Decomposition
Yong Shang, Alexander Heinlein, Siddhartha Mishra +1
Randomized neural networks (RaNNs), in which hidden layers remain fixed after random initialization, provide an efficient alternative for parameter optimization compared to fully p…
PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks
Coen Visser, Alexander Heinlein, Bianca Giovanardi
Physics-Informed Neural Networks (PINNs) have emerged as a tool for approximating the solution of Partial Differential Equations (PDEs) in both forward and inverse problems. PINNs…