Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
J Behler - Physical Chemistry Chemical Physics, 2011 - pubs.rsc.org
The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations
crucially depends on a reliable description of the atomic interactions. A large variety of …
crucially depends on a reliable description of the atomic interactions. A large variety of …
Implicit neural representations with periodic activation functions
Implicitly defined, continuous, differentiable signal representations parameterized by neural
networks have emerged as a powerful paradigm, offering many possible benefits over …
networks have emerged as a powerful paradigm, offering many possible benefits over …
Deepreach: A deep learning approach to high-dimensional reachability
Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for
guaranteeing performance and safety properties of dynamical control systems. Its …
guaranteeing performance and safety properties of dynamical control systems. Its …
Multiscale topology optimization using neural network surrogate models
DA White, WJ Arrighi, J Kudo, SE Watts - Computer Methods in Applied …, 2019 - Elsevier
We are concerned with optimization of macroscale elastic structures that are designed
utilizing spatially varying microscale metamaterials. The macroscale optimization is …
utilizing spatially varying microscale metamaterials. The macroscale optimization is …
A machine learning prediction of academic performance of secondary school students using radial basis function neural network
Background Predictive models for academic performance forecasting have been a useful
tool in the improvement of the administrative, counseling and instructional personnel of …
tool in the improvement of the administrative, counseling and instructional personnel of …
Physics informed neural fields for smoke reconstruction with sparse data
High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a
formidable challenge, due to the complexity of the underlying physics as well as the severe …
formidable challenge, due to the complexity of the underlying physics as well as the severe …
Numerical solution of differential equations using multiquadric radial basis function networks
N Mai-Duy, T Tran-Cong - Neural networks, 2001 - Elsevier
This paper presents mesh-free procedures for solving linear differential equations (ODEs
and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs) …
and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs) …
[HTML][HTML] Inverse differential quadrature method for structural analysis of composite plates
A novel two-dimensional inverse differential quadrature method is proposed to approximate
the solution of high-order system of differential equations. A critical aspect of the proposed …
the solution of high-order system of differential equations. A critical aspect of the proposed …
[HTML][HTML] Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey
Since neural networks have universal approximation capabilities, therefore it is possible to
postulate them as solutions for given differential equations that define unsupervised errors …
postulate them as solutions for given differential equations that define unsupervised errors …
[PDF][PDF] Multiquadric radial basis function approximation methods for the numerical solution of partial differential equations
SA Sarra, EJ Kansa - Advances in Computational Mechanics, 2009 - scottsarra.org
Radial Basis Function (RBF) methods have become the primary tool for interpolating
multidimensional scattered data. RBF methods also have become important tools for solving …
multidimensional scattered data. RBF methods also have become important tools for solving …