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 …
Neural networks with physics-informed architectures and constraints for dynamical systems modeling
Effective inclusion of physics-based knowledge into deep neural network models of
dynamical systems can greatly improve data efficiency and generalization. Such a priori …
dynamical systems can greatly improve data efficiency and generalization. Such a priori …
Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion
Physics-informed neural networks (PINNs) have been proposed to learn the solution of
partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its …
partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its …
Error-bounded learned scientific data compression with preservation of derived quantities
Scientific applications continue to grow and produce extremely large amounts of data, which
require efficient compression algorithms for long-term storage. Compression errors in …
require efficient compression algorithms for long-term storage. Compression errors in …
Complexity of single loop algorithms for nonlinear programming with stochastic objective and constraints
A Alacaoglu, SJ Wright - International Conference on …, 2024 - proceedings.mlr.press
We analyze the sample complexity of single-loop quadratic penalty and augmented
Lagrangian algorithms for solving nonconvex optimization problems with functional equality …
Lagrangian algorithms for solving nonconvex optimization problems with functional equality …
Machine Learning in Computer Aided Engineering
The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …
promoted its introduction in more analytical engineering fields, improving or substituting …
Nonlinear wave evolution with data-driven breaking
Wave breaking is the main mechanism that dissipates energy input into ocean waves by
wind and transferred across the spectrum by nonlinearity. It determines the properties of a …
wind and transferred across the spectrum by nonlinearity. It determines the properties of a …
Invariant preservation in machine learned PDE solvers via error correction
N McGreivy, A Hakim - arXiv preprint arXiv:2303.16110, 2023 - arxiv.org
Machine learned partial differential equation (PDE) solvers trade the reliability of standard
numerical methods for potential gains in accuracy and/or speed. The only way for a solver to …
numerical methods for potential gains in accuracy and/or speed. The only way for a solver to …
[PDF][PDF] NCVX: A general-purpose optimization solver for constrained machine and deep learning
Buyun Liang Plan B MS final defense Page 1 NCVX: A General-Purpose Optimization
Solver for Constrained Machine and Deep Learning Buyun Liang Apr 7, 2023 Page 2 …
Solver for Constrained Machine and Deep Learning Buyun Liang Apr 7, 2023 Page 2 …
[HTML][HTML] Finding a closest saddle–node bifurcation in power systems: An approach by unsupervised deep learning
A Marcial, M Perninge - Electric Power Systems Research, 2024 - Elsevier
We propose a neural network using an unsupervised learning strategy for direct
computation of closest saddle–node bifurcations, eliminating the need for labeled training …
computation of closest saddle–node bifurcations, eliminating the need for labeled training …