Hybrid TimesFM plus ridge regression on covariates forecasts 1-MeV electron flux with average R² of 0.9 on out-of-sample 2024 data, outperforming linear regression, CNN, LSTM and Transformer models.
Physix: A foundation model for physics simulations
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A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
Finetuned physics foundation model generalizes zero-shot from few DNS runs to laboratory RTI data, matching experimental mixing growth rates and handling unseen stable stratification.
WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
A multimodal SwinV2-UNet vision transformer conditioned on data modality and time predicts spatiotemporal fluid flows and reconstructs unobserved fields from limited views using CFD data of argon jet injection.
A fine-tuned video diffusion model becomes a fast, differentiable CFD surrogate for urban wind, enabling gradient-based building-layout optimization confirmed by ground-truth simulations.
Learned PDE solving should target transport over admissible futures via flow learners, not snapshot state regression.
Proposes a cross-layer intellicise network architecture grounded in multiple theories to support intelligent complex systems, with reviews of enabling technologies and a case study.
citing papers explorer
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Forecasting megaelectron-volt electron flux in the Earth's outer radiation belt using supervised machine learning algorithms and a timeseries foundation model
Hybrid TimesFM plus ridge regression on covariates forecasts 1-MeV electron flux with average R² of 0.9 on out-of-sample 2024 data, outperforming linear regression, CNN, LSTM and Transformer models.
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Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
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Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Finetuned physics foundation model generalizes zero-shot from few DNS runs to laboratory RTI data, matching experimental mixing growth rates and handling unseen stable stratification.
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Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
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A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
A multimodal SwinV2-UNet vision transformer conditioned on data modality and time predicts spatiotemporal fluid flows and reconstructs unobserved fields from limited views using CFD data of argon jet injection.
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Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
A fine-tuned video diffusion model becomes a fast, differentiable CFD surrogate for urban wind, enabling gradient-based building-layout optimization confirmed by ground-truth simulations.
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Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
Learned PDE solving should target transport over admissible futures via flow learners, not snapshot state regression.
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Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways
Proposes a cross-layer intellicise network architecture grounded in multiple theories to support intelligent complex systems, with reviews of enabling technologies and a case study.