paper-with-me

홈 › Papers

New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network

2022-12-29 · Xiao Xiong

This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its initial/boundary condition of a certain DE. In section 2, the loss function is generalized to $n^\text{th}$ order ordinary differential equation(ODE). Other methods of construction are examined in Section 3 and applied to three different models to assess their effectiveness.

📄 PDF Abstract BibTeX arXiv:2301.00636

Code (1)

xiaosherry/a-new-designed-loss-function-to-solve-ordinary-differential-equations-with-artificial-neural-network 공식 구현

Similar Papers 제목 키워드 기반

Solving differential equations with unknown constitutive relations as recurrent neural networks

2017-10-06 · Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and we use re…

Time SeriesTime Series Analysis

Symmetry-regularized neural ordinary differential equations

2023-11-28 · Wenbo Hao

Neural ordinary differential equations (Neural ODEs) is a class of machine learning models that approximate the time derivative of hidden states using a neural network. They are powerful tools for modeling continuous-tim…

dNNsolve: an efficient NN-based PDE solver

2021-03-15 · Veronica Guidetti, Francesco Muia, Yvette Welling, Alexander Westphal

Neural Networks (NNs) can be used to solve Ordinary and Partial Differential Equations (ODEs and PDEs) by redefining the question as an optimization problem. The objective function to be optimized is the sum of the squar…

Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

2022-08-26 · Andreas Besginow, Markus Lange-Hegermann

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realization…

Gaussian ProcessesInductive Bias

Elvet -- a neural network-based differential equation and variational problem solver

2021-03-26 · Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky

We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods. Elvet can deal with any system of coupled ordinary or partial differential equations with arb…

BIG-bench Machine Learning