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Found 8 papers, 0 papers with code
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases.
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics.
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods
Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death.
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver.
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
Aortic dissection progresses via delamination of the medial layer of the wall.
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials.
