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Found 15 papers, 9 papers with code
First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces
Physics-informed neural networks have been widely applied to learn general parametric solutions of differential equations.
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences.
RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
However, RC adoption has lagged other neural network models because of the model's sensitivity to its hyper-parameters (HPs).
Physics-Informed Neural Networks for Quantum Eigenvalue Problems
We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems.
One-Shot Transfer Learning of Physics-Informed Neural Networks
In this study, we present a general framework for transfer learning PINNs that results in one-shot inference for linear systems of both ordinary and partial differential equations.
Modeling the effect of the vaccination campaign on the Covid-19 pandemic
Population-wide vaccination is critical for containing the SARS-CoV-2 (Covid-19) pandemic when combined with restrictive and prevention measures.
Unsupervised Reservoir Computing for Solving Ordinary Differential Equations
A closed-form formula for the optimal output weights is derived to solve first order linear equations in a backpropagation-free learning process.
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
In this study, we address the challenge of learning from such non-autonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces.
Encoding Involutory Invariances in Neural Networks
In certain situations, neural networks are trained upon data that obey underlying symmetries.
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function
The proposed neuron was tested for problems where the target was a purely global function, or purely local function, or a composition of two global and local functions.
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves.
Unsupervised Neural Networks for Quantum Eigenvalue Problems
Eigenvalue problems are critical to several fields of science and engineering.
Variational Integrator Graph Networks for Learning Energy Conserving Dynamical Systems
Using this framework we introduce Variational Integrator Graph Networks - a novel method that unifies the strengths of existing approaches by combining an energy constraint, high-order symplectic variational integrators, and graph neural networks.
Hamiltonian neural networks for solving equations of motion
There has been a wave of interest in applying machine learning to study dynamical systems.
LAN -- A materials notation for 2D layered assemblies
Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications.
Materials Science
