Real-time physics simulation of elastic-coupled hydrostatic cellular structures presents computational challenges that preclude direct numerical methods in interactive applications. This paper proposes USFNO-FConvLSTM, a novel neural operator architecture combining U-Shaped Fourier Neural Operators with Factorized Convolutional LSTM for spatio-temporal surrogate modeling of Adaptive Matrix Ecosystem (AME) structures. We develop a four-level fidelity hierarchy enabling context-appropriate precision: L1 lookup tables (<1ms, ±15–30%), L2 empirical models (1–100ms, ±8–15%), L3 ML surrogates (10ms–2s, ±3–8%), and L4 full physics (1s–hours, ±1–5%). The USFNO-FConvLSTM architecture serves as the L3 backbone, achieving 100–1000× speedup over traditional finite element analysis while maintaining engineering-grade accuracy. The architecture innovations include: (1) U-shaped encoder-decoder with skip connections preserving fine-scale features, (2) Fourier layers in the bottleneck enabling resolution-invariant learning, (3) Factorized ConvLSTM for efficient temporal dynamics, and (4) physics-informed loss terms encoding hydrostatic pressure, elastic coupling, and incompressibility constraints. Multi-fidelity transfer learning reduces training data requirements by 50–90% by leveraging abundant low-fidelity (Obi game engine) data to inform scarce high-fidelity (OpenFOAM CFD) targets. Validation demonstrates <5% error on held-out AME configurations with inference times suitable for 60 FPS interactive simulation. The architecture enables deployment via ONNX export to Unity Barracuda for real-time game integration, supporting educational applications from casual learners to professional engineers. Uncertainty quantification via deep ensembles triggers automatic fallback to higher fidelity when prediction confidence is insufficient.
James Otto Danenberg (Thu,) studied this question.