Pages that link to "Item:Q2679296"
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The following pages link to Bayesian physics informed neural networks for real-world nonlinear dynamical systems (Q2679296):
Displaying 30 items.
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- Modeling, analysis and physics informed neural network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction (Q2138895) (← links)
- Conditional physics informed neural networks (Q2247060) (← links)
- A new family of constitutive artificial neural networks towards automated model discovery (Q2679491) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability (Q5109771) (← links)
- Stochastic dynamic analysis of composite plates in thermal environments using nonlinear autoregressive model with exogenous input in polynomial chaos expansion surrogate (Q6084436) (← links)
- Model discovery of compartmental models with graph-supported neural networks (Q6090296) (← links)
- Automated model discovery for skin: discovering the best model, data, and experiment (Q6094670) (← links)
- Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- The \textit{a posteriori} finite element method (APFEM), a framework for efficient parametric study and Bayesian inferences (Q6097616) (← links)
- A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation (Q6118588) (← links)
- Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems (Q6132292) (← links)
- Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread (Q6157162) (← links)
- Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems (Q6399376) (← links)
- Correcting model misspecification in physics-informed neural networks (PINNs) (Q6497270) (← links)
- Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
- A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
- A gradient-enhanced physics-informed neural networks method for the wave equation (Q6583846) (← links)
- Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling (Q6595859) (← links)
- A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks (Q6609808) (← links)
- Forecasting and predicting stochastic agent-based model data with biologically-informed neural networks (Q6632673) (← links)
- Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials (Q6633295) (← links)
- Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning (Q6645133) (← links)
- Auto-weighted Bayesian physics-informed neural networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution (Q6662478) (← links)
- Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators (Q6663284) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
- Discovering uncertainty: Bayesian constitutive artificial neural networks (Q6663336) (← links)
- Taylor series error correction network for super-resolution of discretized partial differential equation solutions (Q6669099) (← links)
