Attention and LoRA regression losses induce Poincaré inequalities under mild regularization, so SGD-mimicking SDEs converge to minimizers with no assumptions on data or model size.
Generative AI for fast and accurate statistical computation of fluids
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FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
WinDiNet repurposes a 2B-parameter video diffusion model as a differentiable surrogate that generates 112-frame urban wind flow rollouts in under one second and enables direct gradient optimization of building positions.
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
GenFocal uses probabilistic ML to downscale coarse climate projections to fine-scale weather events without paired training data and samples rare high-impact events more accurately than prior methods.
citing papers explorer
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Convergent Stochastic Training of Attention and Understanding LoRA
Attention and LoRA regression losses induce Poincaré inequalities under mild regularization, so SGD-mimicking SDEs converge to minimizers with no assumptions on data or model size.
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FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
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Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
WinDiNet repurposes a 2B-parameter video diffusion model as a differentiable surrogate that generates 112-frame urban wind flow rollouts in under one second and enables direct gradient optimization of building positions.
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Neuro-Symbolic AI for Analytical Solutions of Differential Equations
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
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Regional climate risk assessment from climate models using probabilistic machine learning
GenFocal uses probabilistic ML to downscale coarse climate projections to fine-scale weather events without paired training data and samples rare high-impact events more accurately than prior methods.