26 citations · 48 across the 11 of their papers we have counts for
11 papers
Structured AI Demonstrations and Student LLM Use in Engineering Mechanics: Study Design and Preliminary Results
Shuang Geng, Helen Lallos-Harrell, Jiya Ashar +4
The rapid integration of large language models (LLMs) into undergraduate education presents an urgent challenge for engineering instructors. Despite widespread student adoption, th…
MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles
Hiba Kobeissi, Javiera Jilberto, M. Çağatay Karakan +9
Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to reg…
A Data-Driven Computational Model for Engineered Cardiac Microtissues
Javiera Jilberto, Samuel J. DePalma, Jason Lo +5
Engineered heart tissues (EHTs) present a potential solution to some of the current challenges in the treatment of heart disease; however, the development of mature, adult-like car…
Locality sensitive hashing via mechanical behavior
Emma Lejeune, Peerasait Prachaseree
From healing wounds to maintaining homeostasis in cyclically loaded tissue, living systems have a phenomenal ability to sense, store, and respond to mechanical stimuli. Broadly spe…
Investigating Deep Learning Model Calibration for Classification Problems in Mechanics
Saeed Mohammadzadeh, Peerasait Prachaseree, Emma Lejeune
Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applyin…
Towards out of distribution generalization for problems in mechanics
Lingxiao Yuan, Harold S. Park, Emma Lejeune
There has been a massive increase in research interest towards applying data driven methods to problems in mechanics. While traditional machine learning (ML) methods have enabled m…
Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset
Hiba Kobeissi, Saeed Mohammadzadeh, Emma Lejeune
Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tis…
Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks
Peerasait Prachaseree, Emma Lejeune
From designing architected materials to connecting mechanical behavior across scales, computational modeling is a critical tool in solid mechanics. Recently, there has been a growi…
Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels
S. Mohammadzadeh, E. Lejeune
Using simulation to predict the mechanical behavior of heterogeneous materials has applications ranging from topology optimization to multi-scale structural analysis. However, full…
Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes
Bill Zhao, Kehan Zhang, Christopher S. Chen +1
A better fundamental understanding of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has the potential to advance applications ranging from drug discovery t…
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
Emma Lejeune, Bill Zhao
From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution…