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This study systematically evaluates physics-informed neural network techniques for fluid dynamics, showing that combining periodic activations with causal weighting improves performance on the Navier-Stokes vortex shedding benchmark, while further additions cause degradation due to nonlinear interactions.
An individual is seeking a teammate with ML background for the RealPDE Competition at NeurIPS 2026, focusing on Sim2Real and LTTTA tracks with real fluid dynamics data. The registration deadline is August 20.
Physicists have advanced fluid theory by developing a new approach based on symmetries, extending beyond the Navier-Stokes equations and incorporating microscopic particle behaviors.
This paper proposes the Transferable Latent Operator (TLO) for fluid dynamics, enabling zero-shot generalization from Eulerian field prediction to Lagrangian particle rollout without Lagrangian supervision.
This repository presents a proof of concept for a hardware-native optical timing-frozen control plane engine that uses fluid dynamics modeling in GPU registers to reduce jitter in optical data centers for distributed AI architectures.
The paper introduces a hybrid quantum-classical framework enhancing Quantum Physics-Informed Neural Networks (QPINNs) with adaptive collocation point sampling and loss-aware attention for solving differential equations, achieving significant accuracy improvements in fluid dynamics and reaction-diffusion benchmarks.
Researchers at NYU's Courant Institute conducted experiments confirming a 2024 'momentum flux theory' that solves Feynman's reverse sprinkler puzzle, also applying the findings to 'silly sprinklers'.
Researchers discovered that simple, nonelastic liquids can undergo brittle fracture under extensional stress, contradicting prior assumptions that elasticity was necessary for such behavior.
This paper proposes a novel offline reinforcement learning framework for active flow control that uses a sensor position-conditioned architecture with Point Attention layers to handle varying sensor configurations, enabling data-driven policy extraction without costly online interactions.
Linus Ekenstam demonstrates a fluid dynamics designed cup for drinking coffee in space, showcasing how it works in microgravity.
This article explains the physics behind how soccer players can curve their shots in midair, using concepts from fluid dynamics and Newton's laws.
DeepMDMD combines deep learning with algebraic constraints to learn compact, dynamically coherent Koopman operator representations that enforce the product rule as an exact constraint. The method outperforms geometric approaches on high-dimensional chaotic and fluid dynamics problems, reducing spectral pollution and enabling stable long-term forecasting.
This paper investigates the role of group-equivariant architectures in neural fluid dynamics surrogates, introducing the AB-GATr model. It finds that equivariance is beneficial when data lacks strong alignment, but can degrade performance on highly aligned datasets.
A fluid dynamics PhD student used OpenAI's Codex 5.5 model to achieve fluid dynamics control purely through code generation, without training any neural network. It surpassed reinforcement learning baselines in multiple tests, with low cost and interpretable results.
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.
This paper introduces MeLISA, a latent-free autoregressive generative surrogate for forecasting high-dimensional physical dynamics that uses pixel-space MeanFlow to achieve efficient one-step generation. It demonstrates superior long-horizon statistical accuracy and inference speed compared to neural operators on turbulent flow benchmarks.
DeepMind researchers discovered new families of unstable singularities in fundamental fluid dynamics equations using AI techniques, potentially advancing understanding of century-old mathematical problems like the Navier-Stokes equations. The work collaborates with Brown, NYU, and Stanford, revealing patterns in blow-up behavior with unprecedented computational accuracy.
The Well is a large-scale collection of 15TB of diverse physics simulation datasets across 16 domains, designed to benchmark machine learning surrogate models for spatiotemporal physical systems. It provides a unified PyTorch interface and example baselines to accelerate simulation-based workflows.