Interactive design-through-analysis workflows in XR to facilitate computational steering made possible by physics-informed ML, a low-barrier frontend and powerful compute system in the backend.

Computational steering has seen regular incarnations in the Computational Science and Engineering domain with every leap forward in computing and visualisation technologies. While often associated with the ability to interact with large-scale simulations running on remote high-performance compute clusters, this presentation will introduce novel interactive design-through-analysis techniques through visual demonstration with different modalities including XR devices.

The design-through-analysis paradigm means seamless integration of computer-aided design and simulation-based analysis tools so that scientists, engineers & researchers can go back and forth between product design, analysis, and optimisation.

The proposed approach's novelty consists of replacing traditional simulation-based analysis that often hinders rapid design-through-analysis workflows with our recently developed IgANets, which is the embedding of physics-informed machine learning into the Isogeometric Analysis paradigm. More precisely, we train parametrized deep networks to predict solution coefficients of B-Spline/NURBS representations in a compute-intensive offline stage. Problem configurations and geometries are encoded as B-Spline/NURBS objects and passed to the network as inputs, to provide a mechanism for user interaction. Evaluation of IgANets is instantaneous, thereby enabling interactive feedback loops.

In this presentation, we will present a first-of-its-kind demonstrator that couples IgANets, developed at the TU Delft, with a novel user frontend in XR, developed at SURF. Connected with this presentation is the wish to initiate a new trend in computational steering, interactive design-through-analysis.