Pages that link to "Item:Q2237477"
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The following pages link to Solving inverse problems in stochastic models using deep neural networks and adversarial training (Q2237477):
Displaying 18 items.
- Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Isogeometric collocation method for the fractional Laplacian in the 2D bounded domain (Q2180461) (← links)
- Data-driven inverse modelling through neural network (deep learning) and computational heat transfer (Q2236161) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- Solving Bayesian inverse problems from the perspective of deep generative networks (Q2319396) (← links)
- Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures (Q2670339) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- AI in computational mechanics and engineering sciences (Q2693415) (← links)
- Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint (Q5052899) (← links)
- On the use of neural networks for full waveform inversion (Q6096500) (← links)
- Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
- Inferring parameters of pyramidal neuron excitability in mouse models of Alzheimer's disease using biophysical modeling and deep learning (Q6124370) (← links)
- Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems (Q6194148) (← links)
- An end-to-end deep learning approach for extracting stochastic dynamical systems with \(\alpha\)-stable Lévy noise (Q6565156) (← links)
- A deep learning -- genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution (Q6588345) (← links)
