2025-04-09 09:00:00
The crystal plasticity finite element model (CPFEM) is a significant tool in the integrated computational materials engineering (ICME) toolboxes that bridges between microstructures and materials properties relationship. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the numerical…
The problem of damage induced stiffness degradation in composite laminates has been addressed by many approaches ranging from micromechanics to continuum damage mechanics. Most of these approaches are for design purposes but are not useful for inspection of structures during their service life. The…
We describe a new adaptive algorithm for training a shallow neural network based on random Fourier features. We apply the algorithm to learn and approximate dynamics defined by autonomous differential equations. Furthermore, we demonstrate, in computational examples, that the method decreases training time. We…
Traditional material testing methods have been established and standardized for decades to study material capabilities and durability. However, material testing efforts are often laborious as each specimen requires tens to hundreds of repeated tests in order to generate sufficient statistical data. While standard material…
We propose a variational learning strategy for the discovery of non-equilibrium equations, through the variational action density from which these equations may be derived. The strategy is based on the so-called Onsager’s variational principle, which may be written as a function of the free…
The prediction of macro-scale properties of materials requires study of their 3D stochastic microstructures. Since experimentally acquiring a 3D image is often infeasible, computational microstructure reconstruction approaches such as statistical functions-based and machine learning (ML) based methods are used as alternatives to generate 3D…
The stochastic nature of material damage evolution requires the development of both monitoring and modeling methods, capable of providing information related to evolving and multiscale material states. While several monitoring methods related to fracture have been proposed, recent needs for real-time assessment have created…
Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different fields, such as computer vision, natural language processing, and the need for reliable tools in risk-sensitive applications. Recently, various machine learning…
Room temperature mechanical swaging of metallic powders allows the flexible synthesis of fully consolidated, chemically reactive composite powder compacts that can retain nearly the full strength, toughness, density, and machinability of their bulk parent elements. By means of post-mortem soft-catch and recovery of fragmented…
Metallic alloys with superior mechanical properties at elevated temperatures remain in high demand for a variety of applications in aerospace, space, and energy industries. With a high melting temperature (>2400 degree Celsius) and desirable ductility, niobium is a potential candidate for extreme environments that…
