Pages that link to "Item:Q2020738"
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The following pages link to Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (Q2020738):
Displaying 35 items.
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Q2020876) (← links)
- Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction (Q2021272) (← links)
- HiDeNN-TD: reduced-order hierarchical deep learning neural networks (Q2072507) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
- Towards out of distribution generalization for problems in mechanics (Q2083180) (← links)
- A new mesh smoothing method based on a neural network (Q2115586) (← links)
- A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features (Q2115607) (← links)
- A deep learning framework for constitutive modeling based on temporal convolutional network (Q2136467) (← links)
- Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation (Q2138688) (← links)
- Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
- Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
- Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints (Q2142219) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
- A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness (Q2237726) (← links)
- Latent map Gaussian processes for mixed variable metamodeling (Q2246360) (← links)
- Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- AI in computational mechanics and engineering sciences (Q2693415) (← links)
- Using image processing techniques in computational mechanics (Q2697790) (← links)
- Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research (Q6109277) (← links)
- Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty (Q6120135) (← links)
- Isogeometric convolution hierarchical deep-learning neural network: isogeometric analysis with versatile adaptivity (Q6147044) (← links)
- HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis (Q6159332) (← links)
- Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration (Q6164268) (← links)
- Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics (Q6171233) (← links)
- Learned Gaussian quadrature for enriched solid finite elements (Q6171240) (← links)
- I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← links)
- A general framework of high-performance machine learning algorithms: application in structural mechanics (Q6540749) (← links)
- Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
- N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
- Fusing nonlinear solvers with transformers for accelerating the solution of parametric transient problems (Q6566042) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Multi-patch isogeometric convolution hierarchical deep-learning neural network (Q6669070) (← links)
