# uat Published articles for uat. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Meshless Latent Dynamics Models for Model Order Reduction of time-dependent Partial Differential Equations DevFeed: [Meshless Latent Dynamics Models for Model Order Reduction of time-dependent Partial Differential Equations]() Original publisher: [Read original article]() Author: Lorenzo Tomada, Federico Pichi, Gianluigi Rozza Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Finite Element Method (FEM)](), [World models]() Tags: [analysis](), [architectures](), [cs-lg](), [cs-na](), [error](), [experiments](), [grid](), [learning](), [math-na](), [mechanism](), [mesh](), [models](), [neural](), [numerical-analysis](), [partial-differential-equations](), [reduction](), [uat]() ### AI overview The paper presents meshless latent dynamics models for reducing time-dependent PDEs, using neural fields conditioned on a latent state to represent spatially continuous evolution without tying training and inference to a fixed grid. It classifies conditioning as weak, strong, or pure, proves a universal approximation result, and derives Lipschitz stability bounds. The analysis identifies an expressivity-stability trade-off: more expressive conditioning may impair temporal extrapolation. In experiments on Burgers' equation, flow past a cylinder, and linear advection, meshless models reportedly match or exceed mesh-based predictive accuracy with 2% of their trainable parameters and nearly constant error across spatial discretizations. The abstract also reports grid-independent Lipschitz bounds for meshless models, unlike mesh-based counterparts. ### Source excerpt arXiv:2610.41043v1 Announce Type: cross Abstract: Latent dynamics learning has emerged as a prominent approach for Model Order Reduction (MOR) of time-dependent, parameterized Partial Differential Equations (PDEs). Traditional autoencoder-based approaches are intrinsically tied to fixed spatial discretizations during both training and inference, motivating the development of meshless methodologies. In this context, conditioning provides a natural mechanism for representing temporal evolution through spatially continuous functions, enabling mesh-agnostic Latent Dynamics Models (LDMs). In this work, we introduce a unified mathematical framework for mesh-agnostic LDMs based on conditioning in Neural Fields (NeFs). Leveraging a taxonomy of conditioning mechanisms (weak, strong, and pure), we formulate meshless LDMs as NeFs conditioned on a latent state. We establish a Universal Approximation Theorem (UAT) for this class of models and analyze their stability by deriving Lipschitz bounds for different conditioning mechanisms. Our analysis reveals an expressivity--stability trade-off, providing insight into why more expressive conditioning, despite its higher representational capacity, can potentially degrade performance during temporal extrapolation. Finally, we provide a comprehensive comparison of meshless and mesh-based architectures in the continuous limit of the spatial discretization, showing that meshless models guarantee grid-independent Lipschitz bounds, unlike mesh-based counterparts. Our experiments across Burgers' equation, Navier--Stokes flow past a cylinder, and linear advection show that meshless LDMs can achieve predictive accuracy comparable to or better than their mesh-based counterparts while using only 2% of the trainable parameters and exhibiting nearly constant error across spatial discretizations. ## Stop Leaving Quality to QA Alone: Shipping Features Customers Actually Adopt in 2026 DevFeed: [Stop Leaving Quality to QA Alone: Shipping Features Customers Actually Adopt in 2026]() Original publisher: [Read original article]() Author: Susan Isaac Published: 2026-10-23T19:40:29Z Content type: opinion Language: en Sources: [HackerNoon]() Topics: [Product Management](), [User Experience](), [human centric design](), [User interface design]() Tags: [acceptance](), [adoption](), [agile](), [ai](), [align](), [customer](), [customer-experience](), [customers](), [feature-adoption](), [features](), [partnership](), [product](), [product-management](), [product-managers](), [products](), [qa](), [quality](), [quality-assurance](), [real-world](), [shipping](), [software-quality-assurance](), [subject-matter-experts](), [testing](), [uat](), [user](), [user-acceptance-testing]() ### AI overview The article recommends sprint-by-sprint User Acceptance Testing (UAT) as a Product Management and QA partnership to check whether features solve customer problems intuitively, beyond confirming requirements and finding defects. It advises using a stable, customer-like environment with realistic data, interfaces, edge cases and workflows; reserving it for UAT where possible; and inviting employee subject matter experts with customer workflow experience when available. At each sprint's end, PMs and QA should agree on critical workflows, scenarios, expected outcomes and questions, then observe users attempting common tasks without implementation guidance. UAT is for usability and value feedback, while QA owns bugs; Product Managers decide when design or workflow improvements enter development. The article argues earlier feedback is easier to incorporate than UAT held only just before release. ### Source excerpt User Acceptance Testing led by Product Managers in partnership with QA is essential to validate products and ensure real world success