Diff-SPORT is a diffusion-based framework that integrates a generative diffusion prior, maximum a posteriori inference, and Shapley-value attribution to achieve high-fidelity reconstruction of turbulent flows and optimal sensor placement in urban environments. By training the diffusion prior once for a domain, it enables non-linear sensor placement and near-real-time flow reconstruction from sparse measurements, outperforming state-of-the-art methods and running orders of magnitude faster than RANS or LES simulations. The approach also generalizes to experimental passive scalar concentration data, demonstrating up to 57% lower reconstruction error than random sensor placement under extreme sparsity and providing compact, physically interpretable sensor configurations.
arXiv:2601. 11440v3 Announce Type: replace-cross Abstract: Urban wind flow reconstruction is essential for assessing air quality, heat dispersion, and pedestrian comfort, yet remains challenging when only sparse sensor data are available.
arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.
arXiv:2606. 24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal, and physical-variable dimensions.
The study evaluates a data‑driven sparse sensing (DSS) workflow for monitoring urban drainage networks, using a 77‑node model to optimize sensor placement and reconstruct flow conditions. By applying singular value decomposition, pivoted QR selection, and a reconstruction decoder to 225 simulated scenarios, a 3‑node layout (4 % of the network) achieved median Nash‑Sutcliffe efficiency of 0.791, with all cases exceeding 0.700. The method matched performance of Greedy D‑optimal and genetic algorithm approaches, proved robust to Gaussian noise, and identified that sensor loss sensitivity correlates with upstream drainage area and conduit characteristics.
arXiv:2604. 23874v3 Announce Type: replace-cross Abstract: The differentiable physics paradigm may be leveraged as an a-posteriori approach for discovering turbulence closure models by embedding a neural network parameterization directly inside the solver and optimizing it given potentially sparse target data.
arXiv:2603. 21210v3 Announce Type: replace Abstract: Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical.
arXiv:2507. 00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy.
arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.
The book "The Principles of Diffusion Models" outlines the foundational concepts behind diffusion models, tracing their evolution from a forward process that corrupts data into noise to a reverse process that reconstructs data. It presents three complementary perspectives—variational, score-based, and flow-based—each describing how a time-dependent velocity field transports a simple prior to the data distribution. The text also covers practical guidance for controllable generation, efficient solvers, and diffusion-inspired flow-map models, providing a mathematically grounded framework for readers with basic deep‑learning knowledge.
The paper presents a lightweight retraining strategy for a parameterized Reduced Order Model (ROM) that achieves full‑model accuracy using only a fraction of the computational effort and sparse observations. The ROM architecture combines a Variational Autoencoder for dimensionality reduction with a transformer network that evolves latent states while accounting for the Reynolds number as an external control variable. By leveraging the probabilistic VAE, the method generates trajectory ensembles and uncertainty estimates, and adapts to out‑of‑sample parameters through sparse data assimilation with an ensemble Kalman filter, focusing retraining on the autoencoder to correct latent manifold distortions.
arXiv:2606. 11691v1 Announce Type: new Abstract: Latent diffusion and flow matching have emerged as leading approaches for synthetic turbulence generation, yet they systematically under-represent dissipation-range amplitudes.
STITCH-OPE is a model‑based generative framework that uses denoising diffusion to perform off‑policy evaluation (OPE) in high‑dimensional, long‑horizon settings. It generates synthetic trajectories for a target policy by guiding a diffusion model trained on behavior data, subtracting the behavior policy’s score to avoid over‑regularization and stitching partial trajectories to extend horizon length. The authors provide theoretical variance‑reduction guarantees and demonstrate improved mean squared error, correlation, and regret on D4RL and OpenAI Gym benchmarks.