State-of-the-art review of design of experiments for physics-informed deep learning
S Das, S Tesfamariam - arxiv preprint arxiv:2202.06416, 2022 - arxiv.org
This paper presents a comprehensive review of the design of experiments used in the
surrogate models. In particular, this study demonstrates the necessity of the design of …
surrogate models. In particular, this study demonstrates the necessity of the design of …
[HTML][HTML] Multifidelity deep operator networks for data-driven and physics-informed problems
Operator learning for complex nonlinear systems is increasingly common in modeling multi-
physics and multi-scale systems. However, training such high-dimensional operators …
physics and multi-scale systems. However, training such high-dimensional operators …
Finite basis kolmogorov-arnold networks: domain decomposition for data-driven and physics-informed problems
Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to
multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be …
multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be …
Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks
High-fidelity models of multiphysics fluid flow processes are often computationally
expensive. On the other hand, less accurate low-fidelity models could be efficiently executed …
expensive. On the other hand, less accurate low-fidelity models could be efficiently executed …
[PDF][PDF] Multifidelity deep operator networks
AA Howard, M Perego… - arxiv preprint arxiv …, 2022 - app.icerm.brown.edu
Multifidelity Deep Operator Networks Page 1 Multifidelity Deep Operator Networks Amanda
Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …
Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …
Physics-informed deep learning for traffic state estimation: A survey and the outlook
For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …
efficient training (compared to pure deep learning models), physics-informed deep learning …
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
Upcoming technologies like digital twins, autonomous, and artificial intelligent systems
involving safety–critical applications require accurate, interpretable, computationally …
involving safety–critical applications require accurate, interpretable, computationally …
Develo** a physics-informed and physics-penalized neural network model for preliminary design of multi-stage friction pendulum bearings
Over the last few decades, the field of base isolation systems has made significant strides
forward by develo** new systems to improve the behavior of isolated structures under …
forward by develo** new systems to improve the behavior of isolated structures under …
A metalearning approach for physics-informed neural networks (PINNs): Application to parameterized PDEs
Physics-informed neural networks (PINNs) as a means of discretizing partial differential
equations (PDEs) are garnering much attention in the Computational Science and …
equations (PDEs) are garnering much attention in the Computational Science and …
Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics
Physics-informed machine learning (PIML) as a means of solving partial differential
equations (PDEs) has garnered much attention in the Computational Science and …
equations (PDEs) has garnered much attention in the Computational Science and …