# Simulation Simulation is dynamic modeling used to represent and evaluate a system under specified conditions. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## What to Expect at the Zephyr Project Workshop & Meetup (September 24, 2026) - Stuttgart, Germany DevFeed: [What to Expect at the Zephyr Project Workshop & Meetup (September 24, 2026) - Stuttgart, Germany]() Original publisher: [Read original article]() Author: Susan Remmert Published: 2026-09-16T17:57:36Z Content type: article Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Open Source](), [Application Development](), [Testing](), [Simulation](), [Command-line interface]() Tags: [2026](), [blog](), [command-line](), [developers](), [embedded-systems](), [events](), [germany](), [meetup](), [open-source](), [technical](), [workshop](), [zephyr]() ### AI overview The Zephyr community will hold a hands-on workshop and afternoon meetup in Stuttgart, Germany, on September 24, 2026. The workshop will cover practical Zephyr development, while the meetup will feature community-led technical presentations and networking. ### Source excerpt On September 24, the Zephyr community will gather in Stuttgart for a hands-on workshop followed by an afternoon meetup featuring community-led technical presentations. The event is open to experienced contributors, first-time users, students, engineers, and anyone interested in open source embedded development. Attendees may join both sessions or choose either the workshop or the meetup based on their interests. ## What to Expect at the Zephyr Project Workshop & Meetup (September 24, 2026) - Stuttgart, Germany DevFeed: [What to Expect at the Zephyr Project Workshop & Meetup (September 24, 2026) - Stuttgart, Germany]() Original publisher: [Read original article]() Author: Susan Remmert Published: 2026-09-16T17:57:36Z Content type: news Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Embedded Software Dev](), [Application Development](), [Testing](), [Simulation](), [Open Source]() Tags: [2026](), [blog](), [command-line](), [community](), [contributors](), [development](), [embedded](), [embedded-systems](), [event](), [events](), [germany](), [meetup](), [open-source](), [sessions](), [simulation](), [technical](), [testing](), [workshop](), [zephyr]() ### AI overview The Zephyr community will hold a hands-on workshop and afternoon meetup in Stuttgart, Germany, on September 24, 2026. The program includes guided exercises, technical presentations, community networking, and discussions of Zephyr-based embedded applications. ### Source excerpt On September 24, the Zephyr community will gather in Stuttgart for a hands-on workshop followed by an afternoon meetup featuring community-led technical presentations. The event is open to experienced contributors, first-time users, students, engineers, and anyone interested in open source embedded development. Attendees may join both sessions or choose either the workshop or the meetup based on their interests. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## LF Energy Research Finds Open Source Software Can Deliver 2-5x Greater Net Value for Grid Operators DevFeed: [LF Energy Research Finds Open Source Software Can Deliver 2-5x Greater Net Value for Grid Operators]() Original publisher: [Read original article]() Author: andrewb@proximabiz.com (The Linux Foundation) Published: 2026-09-15T07:00:00Z Content type: news Language: en Sources: [Linux Foundation - Blog]() Topics: [Open Source](), [Software](), [cloud-infrastructure](), [digital sovereignty](), [interoperability](), [Simulation]() Tags: [ai-data-centers](), [case-studies](), [compare](), [cost](), [digital-sovereignty](), [framework](), [infrastructure](), [interoperability](), [linux-foundation](), [open-source](), [renewable-energy](), [report](), [research](), [september-2026](), [software](), [summit]() ### AI overview LF Energy reports that open source software can provide grid operators with 2-5 times greater net value than conventional software procurement. Its Open Source Benefit-Cost Framework evaluates total cost of ownership, risk exposure, strategic value, and societal impact using case studies and simulations. ### Source excerpt New benefit-cost framework gives utilities and regulators a standardized methodology to compare open source with conventional software procurement ## An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer DevFeed: [An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-09-14T09:09:42Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [gemma4](), [Large Language Model](), [Raspberry Pi](), [Go Language](), [Simulation]() Tags: [computer](), [data](), [experiment](), [gemma-4](), [go](), [llm](), [patterns](), [raspberry-pi]() ### AI overview The author describes an experiment in which Gemma 4 12B was asked what it would do with its own computer. Running on a Raspberry Pi 1 through a Go harness, the model imagined ingesting large amounts of data, finding patterns, and running large-scale simulations. ### Source excerpt LLM's response ## System helps humans predict when self-driving cars will make mistakes DevFeed: [System helps humans predict when self-driving cars will make mistakes]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-02T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [autonomous vehicles](), [Deep learning](), [Artificial Intelligence](), [Robotics](), [Simulation](), [Computer science]() Tags: [aeronautical-and-astronautical-engineering](), [ai](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [concept-wrapper-network](), [cw-net](), [deep](), [electrical-engineering-and-computer-science-eecs](), [eoin-kenny](), [human-computer-interaction](), [julie-shah](), [laura-major](), [machine-learning](), [mit-schwarzman-college-of-computing](), [momchil-tomov](), [motional](), [research](), [robotics](), [safety](), [school-of-engineering](), [self-driving](), [self-driving-cars](), [simulation](), [transparency]() ### AI overview MIT and Motional researchers developed CW-Net, a method that translates an autonomous vehicle's deep-learning decisions into understandable concepts. Tests found that the explanations helped safety drivers and nonexpert users better predict vehicle behavior. ### Source excerpt A new method, called CW-Net, translates the reasoning process of an autonomous vehicle's AI system into understandable concepts that explain its behavior. ## Isle of Food Combines Island Exploration and Cooking in a Virtual Reality Restaurant Simulation DevFeed: [Isle of Food Combines Island Exploration and Cooking in a Virtual Reality Restaurant Simulation]() Original publisher: [Read original article]() Author: Luis Aviles Published: 2026-08-26T22:57:20Z Content type: opinion Language: en Sources: [UploadVR]() Topics: [Simulation](), [Virtual reality]() Tags: [3d-printer](), [customers](), [games](), [simulation](), [vr-gaming]() ### AI overview The article reviews Isle of Food, a virtual reality cooking and ingredient-collecting simulation on a tropical island. It describes gathering resources, preparing recipes, serving customers, customizing the restaurant, and progressing through experience points and new recipes. ### Source excerpt Isle of Food takes a novel approach to a cooking simulation game by having you explore an island for food to prepare for your guests. ## Qiskit Fermions: a modular toolbox for fermionic systems DevFeed: [Qiskit Fermions: a modular toolbox for fermionic systems]() Original publisher: [Read original article]() Published: 2026-08-24T14:30:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Software](), [Simulation](), [Open Source]() Tags: [open-source](), [quantum](), [quantum-computing](), [quantum-enablement](), [quantum-research](), [quantum-software](), [release](), [simulation](), [software]() ### AI overview IBM introduces Qiskit Fermions, an open-source modular toolbox for expressing fermionic operators and circuits, defining fermion-to-qubit mappings, and compiling fermionic workflows into quantum circuits. The package preserves fermionic structure until transpilation and supports quantum simulation workflows in areas including quantum chemistry, condensed-matter physics, and materials science. ### Source excerpt A new research tool for expressing fermionic operators, circuits, and mappings--and for building efficient fermionic algorithms. ## Coding Challenge #133 - Particle Playground DevFeed: [Coding Challenge #133 - Particle Playground]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-22T08:01:30Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Simulation](), [Cellular automaton](), [coding](), [creative coding](), [Graphics](), [Canvas]() Tags: [canvas](), [coding](), [creative-coding](), [graphics](), [programming](), [real-time](), [simulation]() ### AI overview A coding challenge guides readers through building a 2D particle playground with two simulation engines: a force-based particle system and a cellular automaton. The project combines materials such as sand, water, and fire with a fireworks display whose embers can ignite the material grid. ### Source excerpt This challenge is to build your own particle playground. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## Чем запомнилась ICRA 2026: Reinforcement Learning, генерация сложных сценариев поведения и будущее робототехники DevFeed: [Чем запомнилась ICRA 2026: Reinforcement Learning, генерация сложных сценариев поведения и будущее робототехники]() Original publisher: [Read original article]() Author: egavolk (Яндекс) Published: 2026-08-04T08:00:45Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Reinforcement learning](), [Robotics](), [Simulation](), [Machine learning]() Tags: [2026](), [icra](), [ml](), [reinforcement-learning](), [rl](), [robotics](), [simulation](), [tag-511fbf58fd45](), [tag-6faff4be08e9](), [tag-d704a344cc75](), [tag-dace475544fb]() ### AI overview The article reviews notable trends, papers, and engineering trade-offs discussed at ICRA 2026, with emphasis on reinforcement learning, autonomous-vehicle perception and planning pipelines, simulation, rare edge-case generation, and robotic learning. It also discusses award-winning work on manipulation, humanoid robots, and camera-conditioned policy learning. ### Source excerpt Привет, Хабр! В начале июня в Вене прошла главная международная конференция по робототехнике и автономным системам -- International Conference on Robotics and Automation (ICRA). В этом году среди участников была и наша команда автономного транспорта Яндекса. Топиков, которые обсуждаются на ICRA, много, потому что она не только об ML -- она скорее о робототехнике в целом. Например, есть секции о механизмах и дизайне, а также о медицинских роботах. Было немало и чисто инженерных работ. Ключевой топик докладов на конференции -- RL, он же Reinforcement Learning, обучение с подкреплением. Также нас интересовали статьи по классическому пайплайну автономного автомобиля: perception + prediction + planner + simulation. Новые подходы к Robotic Learning тоже интересны, так как их можно перенести на задачи автономного транспорта. Меня зовут Егор Волков, я занимаюсь претрейном модели планирования движения в автономном транспорте Яндекса. Вместе со мной на конференцию ездил Максим Спорышев -- руководитель службы поведения и предсказания движения. В этой статье мы собрали самые интересные тренды, доклады и инженерные развилки, которые заметили на ICRA 2026, -- от Reinforcement Learning и генерации редких edge-кейсов до того, куда вообще двигается ML в робототехнике. Читать далее ## Relative velocity and closing speed DevFeed: [Relative velocity and closing speed]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-04T03:01:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Simulation](), [Game engine](), [3D]() Tags: [3d](), [game](), [math](), [misc](), [physics]() ### AI overview A tutorial on closing speed, defined as the normal component of the relative velocity between two objects. It explains vector projection, the role of the connecting line between the objects, and how the sign indicates whether they are approaching or separating. ### Source excerpt In Physics simulations or game engines it's sometimes useful to determine the speed with which two objects are approaching each other. This post will discuss the concept of closing speed, which is the normal component of the relative velocity of two objects. Relative velocity and its components Suppose we ... ## Velxio: An open-source browser-based simulator for ESP32 and other embedded boards DevFeed: [Velxio: An open-source browser-based simulator for ESP32 and other embedded boards]() Original publisher: [Read original article]() Author: John Lee Published: 2026-07-31T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32](), [Simulation](), [Open Source](), [Web app](), [browser](), [AI Agent]() Tags: [ai](), [arduino](), [article](), [blog](), [browser](), [education](), [esp32](), [open-source](), [projects](), [simulation](), [web-app]() ### AI overview The article introduces Velxio, an open-source browser-based simulator for embedded projects. It describes real firmware execution on emulated hardware, support for multiple boards and components, analog and digital co-simulation, development workflows, and self-hosting. ### Source excerpt This article introduces Velxio, an open-source, browser-based embedded simulator that runs real ESP32 firmware on emulated hardware. It explains how the simulator works under the hood, demonstrates LED, Wi-Fi, MQTT, and sensor based projects, and highlights support for multiple boards across the ESP32 family. ## Geometry Nodes Physics DevFeed: [Geometry Nodes Physics]() Original publisher: [Read original article]() Author: Jacques Lucke Published: 2026-07-30T14:52:49Z Content type: article Language: en Sources: [Blender]() Topics: [Simulation](), [Framework](), [systems]() Tags: [blender](), [collection](), [experimental](), [features](), [general-development](), [geometry-nodes](), [gravity](), [lts](), [mesh](), [node](), [physics](), [procedural](), [systems](), [vectors](), [workflow]() ### AI overview This article describes Blender 5.2 LTS's new experimental hair and cloth dynamics system built with Geometry Nodes. It explains the declarative XPBD simulation framework, cloth and hair workflows, geometry bundles, and customizable effectors such as colliders, custom forces, and custom behavior closures. ### Source excerpt Geometry Nodes Physics in Blender 5.2 LTS and beyond. ## Quantum advantage through trusted quantum computation DevFeed: [Quantum advantage through trusted quantum computation]() Original publisher: [Read original article]() Published: 2026-07-30T10:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Simulation](), [Benchmark]() Tags: [news](), [process](), [quantum](), [quantum-algorithms](), [quantum-computing](), [quantum-error-correction-mitigation](), [quantum-research](), [reporting](), [science](), [techniques](), [the-result](), [validation](), [verification]() ### AI overview IBM reports three papers demonstrating quantum advantage with built-in validation, including validated error-mitigation techniques and methods for certifying classically hard quantum computations. The article explains how these approaches aim to establish trustworthy results when exact classical verification is unavailable. ### Source excerpt Demonstration shows trusted quantum computation in regimes where classical methods fail. ## SceneSmith uses collaborative AI agents to create 3D environments for robot training DevFeed: [SceneSmith uses collaborative AI agents to create 3D environments for robot training]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-07-13T18:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI research agents](), [robot grasping simulation](), [Simulation](), [vlm](), [Artificial Intelligence](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [3-d](), [3d](), [adversarial-machine-learning](), [agents](), [ai-agents](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [electrical-engineering-and-computer-science-eecs](), [foundation-models](), [general-purpose-robotics](), [gpt-5-2](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [national-science-foundation-nsf](), [nicholas-pfaff](), [research](), [robot-simulations](), [robotics](), [robots](), [russ-tedrake](), [scene-generation](), [scenesmith](), [school-of-engineering](), [simulation](), [simulation-ready-indoor-scenes](), [virtual-playgrounds](), [vision-language-models-vlms](), [zero-shot-policy]() ### AI overview MIT CSAIL and Toyota Research Institute researchers developed SceneSmith, a system that uses three collaborative AI agents to create realistic 3D environments for robot training. The scenes can be loaded into physics simulation software, allowing robots to practice tasks before real-world testing. ### Source excerpt "SceneSmith" system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores. ## AR And VR In Java: ARKit, ARCore, And A Virtual Room You Can Debug DevFeed: [AR And VR In Java: ARKit, ARCore, And A Virtual Room You Can Debug]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-07-12T00:00:00Z Content type: release Language: en Sources: [CodeName One]() Topics: [Java](), [Emulator](), [Simulation](), [Android](), [GPU](), [iOS]() Tags: [android](), [debug](), [emulator](), [event-driven](), [gltf](), [gpu](), [ios](), [java](), [release](), [simulation](), [simulator]() ### AI overview A Codename One release adds portable AR support over ARKit and ARCore, stereo rendering and 360 media support, and a simulated AR backend. Developers can test tracking, planes, anchors, hit testing, and related event-driven code in a virtual room within the simulator. ### Source excerpt World tracking, plane detection, anchors, stereo rendering and 360 panoramas, with a simulated AR room so the whole loop is debuggable in the simulator. ## Outlier Handling at Scale in Experimentation DevFeed: [Outlier Handling at Scale in Experimentation]() Original publisher: [Read original article]() Author: Margarida Moreira da Silva Published: 2026-07-01T13:44:26Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [data](), [Simulation](), [Ground truth / benchmark quality](), [plotting]() Tags: [data](), [experimentation](), [experiments](), [false-positive](), [outlier-detection](), [simulation](), [variance]() ### AI overview The article examines how extreme values affect experimentation at Booking.com. It describes permutation tests and simulated A/A experiments for diagnosing distorted p-value distributions, and reports that increasing outlier magnitude and frequency can cause test failures. ### Source excerpt At Booking.com, thousands of experiments run simultaneously across highly heterogeneous users, from individual travellers to large travel agencies. This means our experiment data regularly contains legitimate but extreme values. When these go unhandled, they distort the statistical conclusions we draw, leading us to scale ideas that don't create value, or to discard ones that do. So, we need outlier handling methods that are reliable, automated, and applicable across diverse metrics without manual intervention. The Problem When extreme values are present in experiment data, they can compromise the estimation of average treatment effects (ATE), leading to unreliable test results and reduced statistical power. Even a single observation can inflate variance enough to mask a real effect or produce a spurious one. In practice, this means we risk shipping changes that appear positive but are not, or killing promising features because noise masked their real effect. At Booking.com's scale, this increase in false conclusions quickly compounds into a meaningful impact on customer experience and business outcomes. A Diagnostic Tool: the Permutation Test One way to assess whether extreme values are distorting results is the permutation test. By permuting over experiment data, we generate hundreds of simulated AA experiments where we know the ground truth: there is no real effect. Plotting the resulting p-values, we expect a uniform distribution. If it instead looks skewed, the underlying data distribution is compromising the validity of results. Plot 1: P-value distributions from simulated A/A tests. Clean normally-distributed estimated effects produce a uniform distribution (left), while the presence of extreme outliers results in skewed p-values (right), indicating a distorted false positive rate.Simulation Evidence: What Drives Failure? We ran AA permutation tests across a range of simulated data distributions to understand when they fail (i.e. not show a uniform p-value di ## A better way to model the behavior of metal alloys DevFeed: [A better way to model the behavior of metal alloys]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-19T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Simulation](), [Machine learning](), [datasets]() Tags: [artificial-intelligence](), [chemical-engineering](), [chemistry](), [computer-modeling](), [computer-science-and-technology](), [daniel-xiao](), [datasets](), [dmse](), [killian-sheriff](), [lewis-r-owen](), [machine-learning](), [materials-science-and-engineering](), [mit-materials-science-and-engineering](), [models](), [phase-diagrams](), [predicting-new-materials](), [predictions](), [research](), [rodrigo-freitas](), [school-of-engineering](), [simulation](), [yifan-cao]() ### AI overview MIT researchers developed a machine-learning approach that uses diverse training datasets to model chemically complex metal alloys and predict their material properties more accurately across different conditions. ### Source excerpt MIT researchers' approach captures subtle atomic patterns, improving predictions of material properties. ## Build Games In Java: Sprites, Box2D Physics And Low-Latency Sound DevFeed: [Build Games In Java: Sprites, Box2D Physics And Low-Latency Sound]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-06-14T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Game Development](), [Java](), [cross-platform](), [Android](), [iOS](), [Simulation]() Tags: [android](), [cross-platform](), [game-development](), [games](), [java](), [low-latency](), [physics](), [windows]() ### AI overview Codename One introduces a com.codename1.gaming package for Java game development, adding a game loop, sprites, pollable input, low-latency sound, and Box2D-based rigid-body physics. The APIs use native compilation to target iOS, Android, and Windows without a JVM at runtime. ### Source excerpt The new com.codename1.gaming package adds a game loop, sprites, pollable input, a low-latency sound pool, and rigid-body physics powered by a bundled Box2D engine, all running unchanged on every platform including iOS. ## Introducing glTF 2.1 with Complex Scenes DevFeed: [Introducing glTF 2.1 with Complex Scenes]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-06-11T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [3D](), [interoperability](), [Tooling](), [Streaming](), [Simulation]() Tags: [3d](), [announce](), [api](), [blog-glt](), [gltf](), [interoperability](), [release](), [streaming](), [tooling]() ### AI overview The Khronos 3D Formats Working Group announces plans for glTF 2.1, a focused and backward-compatible revision designed to support large, composed scenes. Proposed capabilities include multi-file scene graphs, spatial primitives, stable object IDs, progressive 3D streaming support, larger GLB files, and a formally deprecated multiple-scenes pattern. ### Source excerpt Since the release of glTF™ 2.0 in 2017, the format has matured into a rich ecosystem spanning mesh compression, texture optimization, 3D Gaussian splats, and more. Today, the Khronos® 3D Formats Working Group is excited to announce plans for glTF 2.1: a focused, backward-compatible revision of the core specification, built around a single motivation -- making glTF work as well for large, composed scenes as it already does for single assets. Every feature in this release addresses a real gap that today forces teams toward proprietary conventions, custom tooling, or workarounds that break interoperability. ## Netflix's VOID: Fixing the Physics Problem in Video Editing DevFeed: [Netflix's VOID: Fixing the Physics Problem in Video Editing]() Original publisher: [Read original article]() Author: RisingStack Engineering Published: 2026-04-06T22:41:15Z Content type: article Language: en Sources: [RisingStack]() Topics: [AI Models](), [Artificial Intelligence](), [diffusion-transformers](), [Netflix](), [Simulation]() Tags: [ai](), [ai-models](), [diffusion](), [netflix](), [simulation](), [training](), [video]() ### AI overview The article explains Netflix's VOID model for removing objects from videos while also recomputing their downstream physical effects. It describes VOID as a counterfactual video-generation system using video diffusion and training data produced with physics simulation and human-object interaction data. ### Source excerpt There's a familiar trick in modern video editing with AI - taking an object out, slapping some new background in, - and calling it good. It does the trick for simple things, but it all falls apart the moment the object actually starts to move or interact with anything. Take a domino chain for example, [...] The post Netflix's VOID: Fixing the Physics Problem in Video Editing appeared first on RisingStack Engineering. ## Project Genie: Experimenting with infinite, interactive worlds DevFeed: [Project Genie: Experimenting with infinite, interactive worlds]() Original publisher: [Read original article]() Author: Diego Rivas Published: 2026-01-29T17:01:05Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [World models](), [Artificial Intelligence](), [Google AI](), [Simulation](), [Web app]() Tags: [3d](), [ai](), [experimental](), [google](), [google-ai](), [none](), [prototype](), [research](), [research-prototype](), [simulation](), [web](), [web-app](), [world-model]() ### AI overview Google is opening Project Genie to Google AI Ultra subscribers in the U.S. The experimental research prototype, powered by Genie 3, Nano Banana Pro and Gemini, lets users create, explore and remix interactive worlds. ### Source excerpt Google AI Ultra subscribers in the U.S. can try out Project Genie, an experimental research prototype that lets you create and explore worlds. 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