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 …
Physics-informed neural networks with hard linear equality constraints
Surrogate modeling is used to replace computationally expensive simulations. Neural
networks have been widely applied as surrogate models that enable efficient evaluations …
networks have been widely applied as surrogate models that enable efficient evaluations …
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
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 …
[PDF][PDF] Review of machine learning techniques for optimal power flow
ABSTRACT The Optimal Power Flow (OPF) problem is the cornerstone of power systems
operations, providing generators' most economical dispatch for power demands by fulfilling …
operations, providing generators' most economical dispatch for power demands by fulfilling …
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
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 …