Physical Simulations
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Benchmarks
Most implemented
Physics-based Deep Learning
Neural General Circulation Models for Weather and Climate
DiffTaichi: Differentiable Programming for Physical Simulation
4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
Papers
S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation
Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural generators, continue to violate physical laws and simple symbolic rul…
Physical SimulationsVideo Generation4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To accomplish this, agents must translate visu…
Physical SimulationsCode GenerationInductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problems
Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neur…
Physical SimulationsKastor: An efficient fine-tuning strategy for generative emulation of PDE simulations
Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models. How…
Computational EfficiencyPhysical SimulationsPhysics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection
Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and the development of anomaly detection models. However, the severe shortag…
Physical SimulationsAnomaly DetectionHoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations
Standard pipelines for physics-ready 3D reconstruction rely on a decoupled two-stage paradigm: extracting surface geometry followed by an error-prone tetrahedralization process. While recent Lagrangian methods like TetSp…
Physical Simulations3D Reconstruction