Papers Physical Simulations
“Physical Simulations” 태그가 달린 논문 137편 · 필터 해제
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 ReconstructionStitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
While garments are essential for realistic digital humans, their topological variety makes them much harder to model than parametric bodies. Traditional tailoring relies on 2D sewing patterns, yet bridging these patterns…
Garment ReconstructionPhysical SimulationsOne-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields
Physical simulations for intricate geometries with path-dependent constitutive models face difficulties due to the enormous computational cost they require. Recently, the emergence of generative AI models, which succeed …
Physical SimulationsReFPO: Reflow Regularization for Flow Matching Policy Gradients
We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for efficient flow-based control. We uncover a key structural property: the …
Physical SimulationsScene-Level Heterogeneous Physics Simulation with 3D Gaussian Splats
3D Gaussian Splatting (3DGS) has achieved state-of-the-art photorealistic rendering, but the representation gap prevents these assets from being physically interactive. Production-grade physics engines do not understand …
Physical SimulationsSelf-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Control
While neural networks excel in autonomous control, their black-box nature makes control decisions difficult to interpret and diagnose in dynamic fluids. Here, we show how self-evolving scientific agents can design explic…
Reinforcement LearningPhysical SimulationsCode GenerationMPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics
To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (MPM) physical simulations covering rich physical phenomena such as defo…
Physical SimulationsCode GenerationEvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics
While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We present EvoMD-LLM (Evolutionary Molecular Dynamics Large Language M…
Physical SimulationsLearning Orthonormal Bases for Function Spaces
Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear algebraic properties. However, common bases such as Fourier or wavele…
Physical SimulationsVeloGauss: Learning Physically Consistent Gaussian Velocity Fields from Videos
In this paper, we aim to jointly model the geometry, appearance, and physical information of 3D scenes solely from dynamic multi-view videos, without relying on any physical priors. Existing works typically employ physic…
Physical SimulationsM$^3$: Reframing Training Measures for Discretized Physical Simulations
Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to uneven supervision, biasing optimization and causing spatial inconsi…
Physical SimulationsBreaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models …
Physical SimulationsFrom Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Moving beyond traditional qualitative reviews, this work presents a large-sca…
Zero-shot GeneralizationReinforcement LearningPhysical SimulationsLogical ReasoningHow unconstrained machine-learning models learn physical symmetries
The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. …
Physical SimulationsGraph Neural NetworkData AugmentationPoint CloudsLearning Transient Convective Heat Transfer with Geometry Aware World Models
Partial differential equation (PDE) simulations are fundamental to engineering and physics but are often computationally prohibitive for real-time applications. While generative AI offers a promising avenue for surrogate…
Physical SimulationsVideo Generation