We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samples and radii, in line with their individual risk-averse behaviour. By leveraging relevant properties of this class of games, we show that equilibria of the original seemingly infinite-dimensional problem can be obtained as a solution to a finite-dimensional Nash equilibrium problem. We then reformulate the problem as a finite-dimensional variational inequality and establish the connection between the corresponding solution sets. Our reformulation has scalable behaviour with respect to the data size and maintains a fixed number of constraints, independently of the number of samples. To compute a solution, we leverage two algorithms, based on the golden ratio algorithm. The efficiency of both algorithmic schemes is corroborated through extensive simulation studies on an illustrative example and a stochastic portfolio allocation game, where behavioural coupling among investors is modeled.

ParaTools, Inc. Sameer Shende, ParaTools, Inc. Lois Curfman McInnes, Argonne National Laboratory Todd Gamblin, Lawrence Livermore National Laboratory James M. Willenbring, Sandia National Laboratories In this document, we outline key considerations for the next-generation software stack that will support scientific applications integrating AI and modeling & simulation (ModSim) to provide a unified AI/ModSim software stack. The scientific computing community needs a cohesive AI/ModSim software stack. This AI/ModSim stack must support binary distributions to enable emerging scientific workflows. A Cohesive Software Stack for AI and Modeling & Simulation To address future scientific challenges, the next-generation scientific software stack must provide a cohesive portfolio of libraries and tools that facilitate AI and ModSim approaches. As scientific research becomes increasingly interdisciplinary, scientists require both of these toolsets to address complex, data-rich problems in problem domains such as climate modeling, material discovery, and energy optimization.

Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen to predict future states under state-dependent disturbances. We employ recently proposed Unscented Transform-based methods to capture stochasticity in the states as well as the state-dependent switching surfaces. This is in contrast to previous works that perform switching based only on the mean of predicted states. We focus our motion planning application on the navigation of a mobile robot in the presence of dynamically moving agents whose responses are based on sensor-constrained attention zones. We evaluate our framework on a simulated mobile robot and show faster convergence to a goal without collisions when the robot exploits the hybrid human dynamics versus when it does not.

Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate time series forecasting, where available variables in the inference phase are only a subset of the variables in the training phase. VSF presents significant challenges as the entire time series may be missing, and neither inter- nor intra-variable correlations persist. Such conditions impede the effectiveness of traditional imputation methods, primarily focusing on filling in individual missing data points. Inspired by the principle of feature engineering that not all variables contribute positively to forecasting, we propose Task-Oriented Imputation for VSF (TOI-VSF), a novel framework shifts the focus from accurate data recovery to directly support the downstream forecasting task. TOI-VSF incorporates a self-supervised imputation module, agnostic to the forecasting model, designed to fill in missing variables while preserving the vital characteristics and temporal patterns of time series data. Additionally, we implement a joint learning strategy for imputation and forecasting, ensuring that the imputation process is directly aligned with and beneficial to the forecasting objective. Extensive experiments across four datasets demonstrate the superiority of TOI-VSF, outperforming baseline methods by $15\%$ on average.

Numerical modeling errors are unavoidable in finite element analysis. The presence of model errors inherently reflects both model accuracy and uncertainty. To date there have been few methods for explicitly quantifying errors at points of interest (e.g. at finite element nodes). The lack of explicit model error approximators has been addressed recently with the emergence of machine learning (ML), which closes the loop between numerical model features/solutions and explicit model error approximations. In this paper, we propose physics-informed neural networks (PINNs) for simultaneous numerical model error approximation and superresolution. To test our approach, numerical data was generated using finite element simulations on a two-dimensional elastic plate with a central opening. Four- and eight-node quadrilateral elements were used in the discretization to represent the reduced-order and higher-order models, respectively. It was found that the developed PINNs effectively predict model errors in both x and y displacement fields with small differences between predictions and ground truth. Our findings demonstrate that the integration of physics-informed loss functions enables neural networks (NNs) to surpass a purely data-driven approach for approximating model errors.

Kurt'CyberGuy' Knutsson on President-elect Trump's plan to deregulate cryptocurrency and A.I. in his second administration. OpenAI has assembled a "blueprint" for artificial intelligence (AI) infrastructure that the company hopes will be considered by the incoming Trump administration and Congress – suggesting that the plan will help the United States maintain its lead in the field over competitors like China. The company's Vice President of Global Affairs, Chris Lehane, announced the "Infrastructure Blueprint for the U.S." on Wednesday during an event hosted by the Center for Strategic and International Studies (CSIS). The company says AI's potential presents an "unmissable opportunity to revitalize the American Dream and reindustrialize the US." "Investments to extend the current U.S. lead in AI will yield tens of thousands of skilled-trade and other jobs, growth in productivity and GDP; a modernized grid including power generated by nuclear energy; a state-of-the-art network of semiconductor manufacturing facilities; and a new generation of AI-powered businesses and entrepreneurship," OpenAI claims. In this photo illustration, the OpenAI logo is seen displayed on a mobile phone screen with ChatGPT logo in the background.

We present a theoretical model of distributed training, and use it to analyze how far dense and sparse training runs can be scaled. FLOP, two orders of magnitude above the largest training run to date, suggesting the arrival of fundamental barriers to scaling in three years given recent rates of growth. FLOP is infeasible even at low utilization. However, more aggressive batch size scaling and/or shorter and fatter model shapes, if achievable, have the potential to permit much larger training runs. An interactive version of our model will shortly be accessible here. In this work, we address unexamined fundamental questions about limits to scaling in the future: Q1 Given present-day algorithms, GPUs, and interconnects, what is the biggest training run that can be performed within a fixed duration, before intra-and inter-GPU data movement starts to seriously worsen utilization or even render it impossible? Q2 How far might this limit be extended, and what algorithmic or hardware progress can achieve that? Answering these questions empirically would require millions of GPUs and large-scale engineering efforts, so we instead approach them theoretically. In doing so, we develop a simulator that can find optimal training run configurations accounting for the factors that we identify as fundamental. We focus on GPUs, but our theoretical model and findings are broadly applicable to other accelerators, and even groups of accelerators. A2 Improved hardware interconnects may buy no more than two orders of magnitude in training run size, assuming technology anything like the current paradigm. Beyond that, the critical innovations must come from machine learning algorithms: The key challenge is transforming two serial dependencies -- between batches and between layers -- into opportunities for parallelism, by making batch sizes bigger (perhaps enabled by sparsity) and models wider and shallower. Achieving these goals may be quite difficult in practice. However, with innovations in scaling (such as techniques to enable much larger batch sizes) or dramatic increases in network bandwidth coupled with a 10 reduction in interand intra-GPU latency, training runs can be at least a few orders of magnitude larger (right).

This article addresses the challenge of UAV survey coverage path planning for areas that are complex concave polygons, containing exclusion zones or obstacles. While standard drone path planners typically generate coverage paths for simple convex polygons, this study proposes a method to manage more intricate regions, including boundary splits, merges, and interior holes. To achieve this, polygonal decomposition techniques are used to partition the target area into convex sub-regions. The sub-polygons are then merged using a depth-first search algorithm, followed by the generation of continuous Boustrophedon paths based on connected components. Polygonal offset by the straight skeleton method was used to ensure a constant safe distance from the exclusion zones. This approach allows UAV path planning in environments with complex geometric constraints.

Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges and difficulties in integrating effective proprioceptive sensors. Large-scale soft robots ($\approx$ two meters in length) have greater modeling complexity due to increased inertia and related effects of gravity. Common efforts to ease these modeling difficulties such as assuming simple kinematic and dynamics models also limit the general capabilities of soft robots and are not applicable in tasks requiring fast, dynamic motion like throwing and hammering. To overcome these challenges, we propose a data-efficient Bayesian optimization-based approach for learning control policies for dynamic tasks on a large-scale soft robot. Our approach optimizes the task objective function directly from commanded pressures, without requiring approximate kinematics or dynamics as an intermediate step. We demonstrate the effectiveness of our approach through both simulated and real-world experiments.

Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemical and physical properties of elements (such as atomic radius, electronegativity, melting point, and ionization energy), which have a significant impact on material performance. To address this limitation, we first constructed an element property knowledge graph and utilized an embedding model to encode the element attributes within the knowledge graph. Furthermore, we propose a multimodal fusion framework, ESNet, which integrates element property features with crystal structure features to generate joint multimodal representations. This provides a more comprehensive perspective for predicting the performance of crystalline materials, enabling the model to consider both microstructural composition and chemical characteristics of the materials. We conducted experiments on the Materials Project benchmark dataset, which showed leading performance in the bandgap prediction task and achieved results on a par with existing benchmarks in the formation energy prediction task.