A hybrid objective function for robustness of artificial neural networks -- estimation of parameters in a mechanical system

DOI10.1553/etna_vol56s209zbMath1494.93131arXiv2004.07692OpenAlexW3016928826MaRDI QIDQ2672200

U. Schroeder, Hans-Peter Beise, Jan Sokolowski, Volker H. Schulz

Publication date: 8 June 2022

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.07692

zbMATH Keywords

system identificationparameter estimationdynamical systemsmathematical modellingconvolutional neural networkssequential dataprediction robustness

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12) Networked control (93B70)

Uses Software

• MNIST
• word2vec
• ImageNet
• BERT
• torchdiffeq
• AlexNet

Cites Work

• System identification. An introduction.
• Data driven governing equations approximation using deep neural networks
• Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Neural network architectures for parameter estimation of dynamical systems
• An Introduction to Variational Autoencoders
• Data-Driven Identification of Parametric Partial Differential Equations
• Geometric Numerical Integration