Numerical simulation of complex physical phenomena is a core enabling technology for digital twins, which are comprised of physical and virtual assets with a two-way flow of information: data from the physical asset is used to construct and/or calibrate the virtual asset (a numerical model), while numerical predictions from the virtual asset are used for control or decision-making for the physical asset [42]. To be viable for practical application, the virtual asset must be able to produce predictions rapidly and reliably; however, the underlying physics that are of interest for digital twin applications can typically only be accurately simulated using a large number of degrees of freedom, leading to computationally expensive numerical simulations. The explainability and computational efficiency of decisions made by the digital twin play a key role in safety-critical applications, making explainable artificial intelligence an essential ingredient [24]. Model reduction techniques are one such explainable scientific machine learning technique that construct low-dimensional systems, called reduced-order models (ROMs), to serve as computationally inexpensive surrogates for a high-dimensional physics simulation [4, 20]. This paper introduces a technique for adaptively constructing ROMs to emulate systems with parametric dependence, that is, systems whose behavior varies with some set of parameters, usually representing physical properties. We focus on systems where the parametric dependence manifests in the operators defining the model, not merely in initial conditions or external inputs.

While modern multivariate forecasters such as Transformers and GNNs achieve strong benchmark performance, they often suffer from systematic errors at specific variables or horizons and, critically, lack guarantees against performance degradation in deployment. Existing post-hoc residual correction methods attempt to fix these errors, but are inherently greedy: although they may improve average accuracy, they can also "help in the wrong way" by overcorrecting reliable predictions and causing local failures in unseen scenarios. To address this critical "safety gap," we propose CRC (Causality-inspired Safe Residual Correction), a plug-and-play framework explicitly designed to ensure non-degradation. CRC follows a divide-and-conquer philosophy: it employs a causality-inspired encoder to expose direction-aware structure by decoupling self- and cross-variable dynamics, and a hybrid corrector to model residual errors. Crucially, the correction process is governed by a strict four-fold safety mechanism that prevents harmful updates. Experiments across multiple datasets and forecasting backbones show that CRC consistently improves accuracy, while an in-depth ablation study confirms that its core safety mechanisms ensure exceptionally high non-degradation rates (NDR), making CRC a correction framework suited for safe and reliable deployment.

Things to Do in L.A. Tap to enable a layout that focuses on the article. An attendee plays the Monster Hunter Wilds video game on the Sony PlayStation 5 Pro console during the Tokyo Game Show 2024 at Makuhari Messe in 2024 in Chiba, Japan. This is read by an automated voice. Please report any issues or inconsistencies here . With the start of the new year, Californians will pay a new fee every time they buy a product with a nonremovable battery -- whether it's a power tool, a PlayStation or even a singing greeting card.

NVIDIA is showcasing robotics, simulation, gaming and more at CES 2026. During CES 2025, NVIDIA spent much of its keynote touting its leading position in artificial intelligence. Still, the company managed to squeeze in a few notable hardware announcements, including its RTX 5000-series GPUs and Project Digits desktop supercomputer (later redubbed Spark). For this year's show, the company's website says it's lighting up CES 2026 with the power of AI. To that end, NVIDIA is going big in Las Vegas, promising hands-on demos in its Fontainebleau booth, replete with the latest NVIDIA solutions driving innovation and productivity across industries.

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When you purchase through links in our articles, we may earn a small commission. LG's new Gram laptops are light as a feather and tough as Aerominium? LG will be back at CES 2026 with a new LG gram laptops lineup, and, unsurprisingly, it's still all about being as light as humanly possible. LG will be back at CES 2026 with a new lineup of barely-there Gram laptops, and, unsurprisingly, it's still all about being as light as humanly possible. The big change this year is a new material LG cooked up called Aerominum.

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How to watch the Samsung'First Look' CES 2026 presentation Samsung is kicking off CES with a press conference tonight. Samsung is arguably the 800-pound gorilla of CES, since many of the other consumer tech titans -- Apple, Google, Microsoft, Meta, Amazon -- either no longer have a presence, or have never shown up to begin with. In the past, that's given Samsung free range to bow a full spectrum of products that range from phones and computers to refrigerators, AI assistants and rolling robots. But for CES 2026, the company is switching things up a bit: Instead of its longtime midday Monday press conference, the Korean giant will take the lead of the show with a Sunday night presentation. Over the past few weeks, Samsung has been dropping press releases left and right, so we know at least some of what to expect in Vegas this year. Of course, we're holding out hope that we get to hear an update on the Ballie robot -- a star of previous CES presentations that ostensibly missed its previously promised 2025 release date .

Dalrymple said there was a gap in understanding between public sector and AI companies about the power of looming breakthroughs in the technology. Dalrymple said there was a gap in understanding between public sector and AI companies about the power of looming breakthroughs in the technology. World'may not have time' to prepare for AI safety risks, says leading researcher The world "may not have time" to prepare for the safety risks posed by cutting-edge AI systems, according to a leading figure at the UK government's scientific research agency. David Dalrymple, a programme director and AI safety expert at the Aria agency, told the Guardian people should be concerned about the growing capability of the technology. "I think we should be concerned about systems that can perform all of the functions that humans perform to get things done in the world, but better," he said.