Pages that link to "Item:Q2237770"
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The following pages link to Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770):
Displaying 12 items.
- A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model (Q2020945) (← links)
- Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
- A machine learning based plasticity model using proper orthogonal decomposition (Q2184326) (← links)
- An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture (Q2184471) (← links)
- A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths (Q2236174) (← links)
- Numerical Assessment of a Nonintrusive Surrogate Model Based on Recurrent Neural Networks and Proper Orthogonal Decomposition: Rayleigh–Bénard Convection (Q5880415) (← links)
- Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified constitutive relation error framework (Q6497210) (← links)
- Fusing nonlinear solvers with transformers for accelerating the solution of parametric transient problems (Q6566042) (← links)
- FE-LSTM: a hybrid approach to accelerate multiscale simulations of architectured materials using recurrent neural networks and finite element analysis (Q6588352) (← links)
- Operator inference driven data assimilation for high fidelity neutron transport (Q6595882) (← links)
- Predictive modeling of nonlinear system responses using the residual improvement deep learning algorithm (RIDLA) (Q6661867) (← links)
- Application of deep learning reduced-order modeling for single-phase flow in faulted porous media (Q6662482) (← links)
