Search Results for author:
Found 11 papers, 8 papers with code
MicroBundlePillarTrack, A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles
Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity.
Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials
There has been significant recent interest in the mechanics community to design structures that can either violate reciprocity, or exhibit elastic asymmetry or odd elasticity.
Segmenting mechanically heterogeneous domains via unsupervised learning
These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity.
MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles
With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.
A Data-Driven Computational Model for Engineered Cardiac Microtissues
We use this experimental and modeling pipeline to study different mechanical environments, where we contrast the force output of the tissue with the computed active stress of CMs.
Locality sensitive hashing via mechanical behavior
And, there has been a growing interest - inspired in part by the incredible distributed and emergent functionality observed in the natural world - in exploring the ability of engineered systems to perform computation through mechanisms that are fundamentally driven by physical laws.
Investigating Deep Learning Model Calibration for Classification Problems in Mechanics
Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics.
Towards out of distribution generalization for problems in mechanics
To address this, we investigate the performance of OOD generalization methods for regression problems in mechanics.
Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset
In general, diverse generated patterns with adequate resemblance to the real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset.
Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks
Notably, while machine learning approaches that rely on Graph Neural Networks (GNNs) have shown success in learning mechanics, the performance of GNNs has yet to be investigated on a myriad of solid mechanics problems.
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
Then, we show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations.
