the Variational Flow
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A Unifying View of Variational Generative Wasserstein Flows
arXiv:2605.31369v1 Announce Type: new Abstract: Many modern generative models can be viewed as minimizing divergences between probability distributions, yet they rely on different algorithmic and geometric principles. Wasserstein gradient flows provide a continuous-time formulation for optimizing over distributions, and can be approximated through their implicit discretization via the Jordan-Kinderlehrer-Otto (JKO) scheme. In this work, we present a unified theoretical framework for...
PF$\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power...
Flow-Transformed Implicit Processes for Function-Space Variational Inference
Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performing posterior inference with such priors is challenging because their induced function-space distributions are typically not available in closed form. One practical strategy is to approximate the prior using a finite collection of sampled functions, and then represent posterior functions...
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
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A variational formulation of the adjoint Kutta condition in potential flow
arXiv:2606.06937v1 Announce Type: new Abstract: We give a variational formulation of the continuous adjoint Kutta condition for two-dimensional subcritical potential flow, with emphasis on the Kutta condition and the role of the wake. We show that the adjoint Kutta condition can be imposed by a penalty term evaluated at the trailing edge, with the corresponding Lagrange multiplier determined by stationarity of the Lagrangian with respect to circulation, and that a wake treatment is not...
Auxiliary Gradient-Flow Solvers for Generalized Newtonian Models
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Pauli propagation enables fast classical simulation of strongly correlated quantum systems
Announce Type: replace-cross Abstract: Ground state energy estimation for strongly correlated quantum systems remains a central challenge in computational physics and chemistry. While tensor network methods like DMRG provide efficient solutions for one-dimensional systems, higher-dimensional problems remain difficult. Here we present a variational double bracket flow (vDBF) algorithm that leverages Pauli Propagation, a technique originally developed for classical simulation of quantum...
Transverse spin texture in optical non-Hermitian skin modes
arXiv:2606.02189v1 Announce Type: new Abstract: In structured electromagnetic fields, polarization textures are often closely linked to the spatial variation of the energy flow. However, this familiar picture has been established mainly for lossless and isotropic settings, and concrete examples showing how it is modified in media with gain and loss remain limited. Here, we demonstrate that optical skin modes associated with the non-Hermitian skin effect (NHSE) carry a finite transverse...
Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective
Announce Type: replace Abstract: Generative Modeling via Drifting~\citep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drift operator, yet its success is largely empirical and its theoretical foundations remain poorly understood. We observe that \emph{under a Gaussian kernel, the drift operator is exactly a score difference on smoothed distributions}. This answers three questions left open in the original work: (1) whether a...
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
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