2025-04-09 09:00:00
We developed an image-based convolutional neural network (CNN) designed for quantitative, time-resolved measurement of fragmentation behavior in opaque brittle materials using ultra-high-speed optical imaging. Building on prior work with the U-net model, we trained binary, 3-class, and 5-class models via supervised learning, using data…
The high computational cost of finite element analysis (FEA) often limits its application to heterogeneous and complex materials, where intricate behaviors and interactions lead to time-extensive and computationally intensive simulations. As a result, data-driven approaches based on deep learning (DL) have emerged as promising,…
Recent advances in scientific programming, particularly with regards to topology optimization and machine learning, have necessitated computational methods that generate gradients for optimization. One method to do so, is to utilize a tool called automatic differentiation, a mechanism to algorithmically calculate derivatives of functions…
Engineering material microstructure is the key to developing advanced materials with enhanced mechanical properties required for advanced applications. At the same time, conventional mechanics modeling finds it challenging in the context of high-throughput material design, wherein it needs to predict the microstructure effects on…
Designing multiscale structures entails several challenging tasks, including generating random material microstructures, developing microstructure-resolved physics models, and performing multiscale design and optimization. Conventionally, multiscale finite element (FE) methods are implemented to perform microstructure-resolved physics simulations. However, these slow and computationally expensive numerical methods often…
Neural operators have recently emerged as a powerful machine-learning framework for learning infinite-dimensional mappings in physics-based systems described using partial differential equations. However, their application to realistic high-dimensional problems often encounters computational challenges. Existing approaches either down-sample the input-output spaces, sacrificing accuracy by overlooking…
Deep operator networks (DeepONets) have shown significant potential in solving partial differential equations (PDEs) by utilizing neural networks to learn mappings between function spaces. However, their performance declines as system size and complexity increase. To address this, recent Latent DeepONet models have shown promise…
Morphing wing technology holds the potential to revolutionize aviation by dramatically improving fuel efficiency, enhancing operational flexibility, and enabling unparalleled versatility for both military and civilian applications. Recent advances in materials science and engineering have driven the development of morphing wings equipped with active…
We introduce HYDRA, a learning algorithm that generates symbolic hyperelasticity models designed for running in 3D Eulerian hydrocodes that require fast and robust inference time. Classical deep learning methods require a large number of neurons to express a learned hyperelasticity model adequately. Large neural…
Understanding the crystallization kinetics of homopolymers is important for the design and manufacturing of recycled polymers with targeted properties. Analyzing Polarized Optical Microscopy (POM) images is widely used in investigating the morphology and micro-structure of polymers, which is often done manually in experiments, time-consuming…
