| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
The Variational Deep Materials Network: Efficient Extrapolation With Uncertainty of Homogenized Material Responses |
| Author(s) |
Andreas E. Robertson, Dongil Shin, Remi Dingreville |
| On-Site Speaker (Planned) |
Andreas E. Robertson |
| Abstract Scope |
Surrogate models are a fundamental component in any machining learning framework for materials science. They provide the necessary computational efficiency for many downstream tasks, e.g., optimization in design. Importantly, useful surrogate models must be developed to account for both uncertainty and limited data. The Deep Material Network is a physics-informed machine learning framework that can stably extrapolate to predict non-linear homogenized material responses even though it is trained on only cheap elastic data. We present our extension: the Variational DMN. The VDMN naturally accounts for aleatoric microstructural uncertainty in its prediction. Importantly, this uncertainty prediction also extrapolates, allowing the VDMN to quantify uncertainty in both linear and nonlinear material responses without the need for nonlinear data. We present the algorithmic advances necessary for these changes and then present a series of examples exploring the strengths and limitations of the VDMN as a tool for accelerated uncertainty quantification in materials science. |
| Proceedings Inclusion? |
Undecided |