2023-04-05 08:00:00
Building models for the plasticity, thermodynamics and kinetics of metals is challenging as subtle aspects of atomic cohesion must be faithfully reproduced, and predictions often require averaging over large, complex configuration ensembles. I will discuss how the energy landscapes of atomic systems can be…
The scope of the present study is to develop a stochastic hierarchical multiscale computational framework for the analysis and simulation of heterogeneous materials. For the uncertainty quantification and the surrogate modeling of multi-scale materials in a hierarchical scheme, two distinct computational frameworks are being…
Despite significant research efforts through programs such as the Materials Genome Initiative, the quantitative design and discovery of engineering materials remain a challenging and slow process. The primary roadblocks remain the complexity of the design spaces involved (e.g., the space of material microstructures) and…
This talk concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e.g., with only statistical moments and/or on a coarse topology) rather than fully specified. Such partial knowledge provides constraints…
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…
