Manifold embedding data-driven mechanics

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

(2021) Yousef Heider et al.   INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS

A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data

(2021) Bahador Bahmani et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Efficient data structures for model-free data-driven computational mechanics

(2021) Robert Eggersmann et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions

(2021) Alexander Fuchs et al.   COMPUTERS & STRUCTURES

Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

(2021) Xiaolong He et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-Driven nonlocal mechanics: Discovering the internal length scales of materials

(2021) K. Karapiperis et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A physics-constrained data-driven approach based on locally convex reconstruction for noisy database

(2020) Qizhi He et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A kernel method for learning constitutive relation in data-driven computational elasticity

(2020) Yoshihiro Kanno   JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS

Variational framework for distance-minimizing method in data-driven computational mechanics

(2020) Lu Trong Khiem Nguyen et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A framework for Data-Driven Structural Analysis in general elasticity based on nonlinear optimization: The static case

(2020) Cristian Guillermo Gebhardt et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep learning models for global coordinate transformations that linearise PDEs

(2020) CRAIG GIN et al.   EUROPEAN JOURNAL OF APPLIED MATHEMATICS

Model-free data-driven computational mechanics enhanced by tensor voting

(2020) Robert Eggersmann et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-driven fracture mechanics

(2020) P. Carrara et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Hyperparameters and tuning strategies for random forest

(2019) Philipp Probst et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Linearly Recurrent Autoencoder Networks for Learning Dynamics

(2019) Samuel E. Otto et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Model-Free Data-Driven inelasticity

(2019) R. Eggersmann et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Reconciling modern machine-learning practice and the classical bias–variance trade-off

(2019) Mikhail Belkin et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

(2019) Kookjin Lee et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-Driven Problems in Elasticity

(2018) S. Conti et al.   ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

(2018) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-based derivation of material response

(2018) Adrien Leygue et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A data-driven approach to nonlinear elasticity

(2018) Lu Trong Khiem Nguyen et al.   COMPUTERS & STRUCTURES

Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations

(2018) D.G. Giovanis et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach

(2018) Liang Lan et al.   IEEE Transactions on Neural Networks and Learning Systems

Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach

(2018) Yoshihiro Kanno   JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS

Deep learning for universal linear embeddings of nonlinear dynamics

(2018) Bethany Lusch et al.   Nature Communications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven computing in dynamics

(2017) T. Kirchdoerfer et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Mixed Arlequin method for multiscale poromechanics problems

(2017) WaiChing Sun et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

(2017) Karel Matouš et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

(2016) Rubén Ibañez et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain

(2016) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-driven computational mechanics

(2016) T. Kirchdoerfer et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

IDENTIFYING MATERIAL PARAMETERS FOR A MICRO-POLAR PLASTICITY MODEL VIA X-RAY MICRO-COMPUTED TOMOGRAPHIC (CT) IMAGES: LESSONS LEARNED FROM THE CURVE-FITTING EXERCISES

(2016) Kun Wang et al.   International Journal for Multiscale Computational Engineering

Determining Material Parameters for Critical State Plasticity Models Based on Multilevel Extended Digital Database

(2015) Yang Liu et al.   JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME

A multiscale overlapped coupling formulation for large-deformation strain localization

(2014) WaiChing Sun et al.   COMPUTATIONAL MECHANICS

Riemannian Manifold Learning

(2008) Tong Lin et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

On Atomistic-to-Continuum Coupling by Blending

(2008) Santiago Badia et al.   MULTISCALE MODELING & SIMULATION