Data-driven modeling of partially observed biological systems (Q6537200)

scientific article; zbMATH DE number 7846939

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English	Data-driven modeling of partially observed biological systems	scientific article; zbMATH DE number 7846939

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instance of

scholarly article

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title

Data-driven modeling of partially observed biological systems (English)

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Communications on Applied Mathematics and Computation

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publication date

14 May 2024

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zbMATH Keywords

deep neural network (DNN)

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governing equation discovery

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biological system

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partial observation

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MaRDI profile type

Publication

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Mathematical analysis of a proposed mechanism for oscillatory insulin secretion in perifused HIT-15 cells

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DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION

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Data driven governing equations approximation using deep neural networks

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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations

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An artificial neural network as a troubled-cell indicator

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Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

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Detecting Causality in Complex Ecosystems

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

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Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

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Data-driven deep learning of partial differential equations in modal space

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Identifiers

Mathematics Subject Classification ID

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10.1007/S42967-023-00317-2

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Sitelinks

Mathematics(1 entry)

mardi Publication:6537200