18 citations · 20 across the 6 of their papers we have counts for
6 papers
FEM-Bench: A Structured Scientific Reasoning Benchmark for Evaluating Code-Generating LLMs
Saeed Mohammadzadeh, Erfan Hamdi, Joel Shor +1
As LLMs advance their reasoning capabilities about the physical world, the absence of rigorous benchmarks for evaluating their ability to generate scientifically valid physical mod…
SarcGraph for High-Throughput Regional Analysis of Sarcomere Organization and Contractile Function in 2D Cardiac Muscle Bundles
Saeed Mohammadzadeh, Yao-Chang Tsan, Aaron Renberg +3
Timelapse images of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide rich information on cell structure and contractile function. However, it is chall…
Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph
Saeed Mohammadzadeh, Emma Lejeune
In cardiac cells, structural organization is an important indicator of cell maturity and healthy function. Healthy cardiomyocytes exhibit well-aligned morphology with densely packe…
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…
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…
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…