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This paper proposes a method for simulating large LLM-agent societies on a laptop by fitting low-parameter surrogate models from a few hundred queries, using a statistical-physics-based taxonomy to predict when this approximation holds. The approach is validated on EconAgent and several other simulations using DeepSeek-elicited agent behaviors.
The paper develops neural operator surrogates to predict hydrodynamic fields (velocity, vorticity, pressure) around immersed-boundary soft swimmers, achieving low global relative errors on held-out trajectories while identifying pressure accuracy and physical consistency as areas for further work.
This paper proposes a Neural Controlled Differential Equation (Neural CDE) framework for learning continuous-time surrogate models of grid-forming inverters for EMT simulation, incorporating dual slow/fast pathways and physics-inspired regularization to capture multi-time-scale dynamics.
This paper presents a physics-informed neural network (PINN) framework for modeling transient elastodynamic wave propagation in bimaterial systems, using a steel-aluminum specimen from a Split Hopkinson Pressure Bar. The PINN accurately predicts wave transmission and reflection, validated against high-fidelity finite-element simulations, and serves as a continuous surrogate model for elastodynamic analysis.
This paper proposes a data-driven surrogate modeling framework using a hybrid Graph Neural Network-Long Short-Term Memory architecture to predict the static response of additively manufactured short-fiber thermoplastics, achieving high accuracy (R²≈0.98) and two orders of magnitude speedup over finite element simulations.
Physics-conforming Latent Twins is a framework for learning latent surrogate solution operators that enforce physical principles such as conservation laws and dissipative inequalities by design, using a constraint-transfer approach and structure-preserving latent dynamics.
Researchers from MIT present a methodology for inverse design of nuclear critical experiments using deep neural networks with a novel multigroup attention pooling architecture and gradient-based optimization to maximize neutronic similarity coefficients. The approach is applied to validate a HALEU fuel transportation cask, achieving high similarity scores for three configurations of interest.
This paper presents a comprehensive mathematical framework for sequential surrogate modeling of three-phase black-oil reservoir dynamics using Fourier Neural Operators (FNO) and physics-informed variants (PINO), applied to the Norne benchmark reservoir. Theoretical contributions include functional-analytic formulation, covariate shift analysis, physics-constrained spectral stability, and truncated backpropagation gradient analysis.
Proposes two-parameter flows to learn the dynamics of high-dimensional probability densities from unlabeled samples, using conditional flow matching to extract physics-time velocity fields.
This paper introduces Mask-Morph Graph U-Net (MMGUNet), a graph neural network-based surrogate model for crashworthiness field prediction that addresses geometric generalisability via coarse-graph morphing and masked pretraining.
This paper proposes an active learning framework to couple high-fidelity Modelica simulations with simpler surrogate models (SINDyC, FNN, GRU) for creating efficient digital twins of thermal energy distribution systems. The approach significantly reduces the number of simulation trajectories needed while maintaining predictive accuracy and enabling uncertainty quantification.
This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.
Researchers propose a surrogate modeling framework to quantify and interpret latent medical knowledge encoded in black-box LLMs, revealing both valid associations and persistent racial biases.