Data-driven modeling of partially observed biological systems
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Publication:6537200
DOI10.1007/S42967-023-00317-2MaRDI QIDQ6537200
Wei-Hung Su, Ching-Shan Chou, Dongbin Xiu
Publication date: 14 May 2024
Published in: Communications on Applied Mathematics and Computation (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Qualitative investigation and simulation of ordinary differential equation models (34C60) Systems biology, networks (92C42)
Cites Work
- Title not available (Why is that?)
- Detecting Causality in Complex Ecosystems
- Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- Machine learning of linear differential equations using Gaussian processes
- A machine learning approach for efficient uncertainty quantification using multiscale methods
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Mathematical analysis of a proposed mechanism for oscillatory insulin secretion in perifused HIT-15 cells
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- Data-driven deep learning of partial differential equations in modal space
- On generalized residual network for deep learning of unknown dynamical systems
- Deep neural network modeling of unknown partial differential equations in nodal space
- Data driven governing equations approximation using deep neural networks
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An artificial neural network as a troubled-cell indicator
- Equation-free, coarse-grained multiscale computation: enabling microscopic simulators to perform system-level analysis
- Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations
- Automated reverse engineering of nonlinear dynamical systems
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability
- Solving high-dimensional partial differential equations using deep learning
- Data-Driven Learning of Nonautonomous Systems
- Solving parametric PDE problems with artificial neural networks
- DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
- Transport, Collective Motion, and Brownian Motion
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