Pages that link to "Item:Q2184326"

The following pages link to A machine learning based plasticity model using proper orthogonal decomposition (Q2184326):

Displaying 41 items.

• MAP123-EPF: a mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain (Q2020782) (← links)
• A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model (Q2020945) (← links)
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
• Interaction-based material network: a general framework for (porous) microstructured materials (Q2072441) (← links)
• Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm (Q2096817) (← links)
• A data-driven approach for modeling tension-compression asymmetric material behavior: numerical simulation and experiment (Q2115577) (← links)
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
• Model-data-driven constitutive responses: application to a multiscale computational framework (Q2234818) (← links)
• Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites (Q2236305) (← links)
• Data-driven reduced homogenization for transient diffusion problems with emergent history effects (Q2236925) (← links)
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
• Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770) (← links)
• Data driven modeling of plastic deformation (Q2309802) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Data driven modeling of interfacial traction-separation relations using a thermodynamically consistent neural network (Q2678537) (← links)
• A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments (Q2686894) (← links)
• Enhancing phenomenological yield functions with data: challenges and opportunities (Q2692822) (← links)
• Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
• Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
• What Machine Learning Can Do for Computational Solid Mechanics (Q5051038) (← links)
• Computational Homogenization Using Convolutional Neural Networks (Q5051078) (← links)
• Numerical Assessment of a Nonintrusive Surrogate Model Based on Recurrent Neural Networks and Proper Orthogonal Decomposition: Rayleigh–Bénard Convection (Q5880415) (← links)
• A micromechanics‐based recurrent neural networks model for path‐dependent cyclic deformation of short fiber composites (Q6062835) (← links)
• An adaptive reduced order model for the angular discretization of the Boltzmann transport equation using independent basis sets over a partitioning of the space‐angle domain (Q6070103) (← links)
• Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks (Q6084775) (← links)
• Deep multimodal autoencoder for crack criticality assessment (Q6089256) (← links)
• Reduced order mathematical homogenization method for polycrystalline microstructure with microstructurally small cracks (Q6091394) (← links)
• Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement (Q6096450) (← links)
• Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation (Q6096506) (← links)
• Incompressible rubber thermoelasticity: a neural network approach (Q6101617) (← links)
• A machine-learning aided multiscale homogenization model for crystal plasticity: application for face-centered cubic single crystals (Q6159320) (← links)
• A framework for neural network based constitutive modelling of inelastic materials (Q6194141) (← 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)
• Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
• An assessment of shallow neural networks for stress updates in computational solid mechanics (Q6572482) (← links)
• Recurrent neural networks and transfer learning for predicting elasto-plasticity in woven composites (Q6586368) (← links)
• An Eulerian constitutive model for rate-dependent inelasticity enhanced by neural networks (Q6595904) (← links)
• Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)