论文检索

Brandon Yee, Wilson Collins, Caden Wang, Mihir Tekal

Phase transitions in condensed matter systems traditionally require prior knowledge of order parameters for identification. We present Prometheus, a variational autoencoder framework for unsupervised discovery of phase transitions and order parameters in the two-dimensional Ising model without prior physical knowledge. Our approach combines convolutional neural networks with beta-variational autoencoders to learn compressed representations that naturally separate ordered and disordered phases. Experimental validation demonstrates automatic discovery of the order parameter with 0.85 correlation to theoretical magnetization and critical temperature detection within 0.27% of the theoretical value, achieving 89% improvement over principal component analysis while requiring no supervision.

Atul Teja Vellampalli, Yugan Kumar, Kai Zhe Tan, Kok Yang Tan, Vanessa Koh, Rahul Malhotra, Angelique W. M. Chan, David Matchar, Navrag B. Singh, Sai G. S. Pai

Falls are a major cause of injury and loss of independence among older adults, making prevention a critical priority for healthy aging. Early detection of fall risk through screening can enable timely interventions that reduce these adverse out-comes. Traditional clinical methods, such as using the history of falls and simple questionnaire-based screening, provide a quick and low-cost means of assessment but often have poor predictive accuracy and fail in presence of missing information. To support cost-effective screening and intervention, there is a need for tools that can accurately assess fall-risk in presence of missing information with better accuracy than cur-rent approaches. In this study, we developed a k-Nearest Neighbors (kNN) model that predicts whether an older adult will experience at least one fall within 12 months after baseline assessment, while simultaneously imputing missing data. Using data from 2,291 community-dwelling older adults in Singapore and 317 features spanning gait, cognition, physical activity, and comorbidities, our model achieved an AUC = 0.62 and F1 = 0.40, a significant improvement over the current clinical standard based solely on fall history (AUC ≈ 0.50). This model offers a more cost-effective screening tool for large-scale community deployment and highlights the feasibility of light-weight, imputation-aware models for practical fall-risk screening in aging populations.

Rohan Saha, Tushar Shinde

Building temperature prediction is crucial for energy optimization and control in smart cities. We present a physics-enhanced XGBoost framework in a multi-stage sequential scaling approach. Starting from single-zone, single-day predictions, we progressively scale to multi-zone, multi-year forecasts using real-world data from Google's Smart Building Simulator. Our method incorporates physics-enhanced features, temporal encodings, and inter-zone interactions, achieving mean absolute errors (MAE) as low as 0.169°F for weekly multi-zone predictions. For longer horizons, we employ ensemble strategies, demonstrating robust performance up to 2.5 years. Compared to baseline models, our framework consistently improves long-term prediction fidelity. This work advances urban AI by enabling accurate long-term building dynamics modeling for downstream control tasks and bridges machine learning with physics-based modeling approaches.

Benjamin Richards, Pushpa Kumar Balan

Deep learning models are emerging as strong alternatives to numerical weather prediction, yet their internal representations remain poorly understood. We analyze the latent space of Microsoft’s Aurora model to test whether its embed- dings align with known physical processes. First, we show that land–sea distinctions are strongly captured, with errors mainly at coastlines. Second, we examine extreme surface temperatures using percentile-based thresholds, finding that embeddings reveal a gradient from moderate to severe events, though recall degrades at the rarest percentiles. These results suggest that Aurora’s encoder encodes physically consistent features but underestimates rare extremes. Our study combines deep learning forecasting, interpretable representation learning, and classical ML probing, illustrating how cross-disciplinary AI methods can yield insight into foundation models

Ashikur Nobel, Jacob Matos, Honggang Wang, Hua Fang

We present iDT-diet, an intelligent digital twin prototype designed to model the long-term influence of diet quality on health biomarkers and chronic conditions. The system integrates three novel components: (i) a random forest learning model enhanced with Choquet LASSO feature selection for capturing complex, nonlinear interactions in temporal health data; (ii) a translation module that converts predictive outputs into natural language narratives of physical and biomarker states; and (iii) a generative 3D visualization engine that produces dynamic, personalized digital twins reflecting evolving health trajectories. This integration uniquely links advanced machine learning, interpretable communication, and immersive visualization within a single framework. While the current implementation focuses on retrospective digital twin generation, the system architecture supports real-time data integration, enabling continuous monitoring, predictive simulation, and personalized recommendation delivery for diet and lifestyle management.

Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman Chakraborty

This study develops a physics-informed neural network (PINN) framework to predict droplet spreading dynamics on unstructured rough surfaces. The trained model effectively captures temporal evolution of the droplet shape, contact line motion, and interfacial deformation. This integration of multiphase physics with neural networks provides a mesh-free and computationally efficient alternative to numerical solvers, enabling rapid analysis and design of wettability-controlled surfaces, microfluidic devices.

Taqiya Ehsan, Shuren Xia, Jorge Ortiz

Embodied agents must reason causally, as correlation-based models fail under intervention and distribution shift. This challenge arises in domains like robotics and cyber-physical systems, where agents balance efficiency and comfort under uncertainty. We introduce POLICYGRID, unifying causal discovery and control by treating each action as both decision and experiment. Leveraging constraint-based search, neural causal models, and language model priors with interventional validation, POLICYGRID yields adaptive, interpretable policies. Across synthetic, real-world, and live deployments, it achieves superior causal recovery (F1 = 0.89) and 2.8× better multi-objective performance than correlation-based baselines, demonstrating safe, generalizable decision-making.

Oliver Chang

Autonomous driving has shown significant progress in recent years. The combination of advanced sensors, ample data, and machine learning algorithms has led to the deployment of autonomous vehicles (AVs) in cities like Los Angeles, San Francisco, and Phoenix. While not all humans can drive perfectly, AVs should be able to plan, adapt, and react to environmental disturbances, including irrational human drivers. My research focuses on applying reinforcement learning (RL) techniques to validate AV-related cyber-physical systems (CPS) in realistic environments. I develop a custom RL environment that simulates highway driving scenarios with multiple vehicles. This environment includes a CPS model of adaptive cruise control (ACC), a lane-changing model (MOBIL), and an adversarial agent that learns to drive irrationally. My work extends interpretable RL techniques to continuous control tasks like autonomous driving.

Jiajun Wu, Yunzhi Zhang, Hong-Xing Yu, Joy Hsu, Jiayuan Mao

This perspective article discusses an emerging research direction: to what extent can foundation models yield usable structure for modeling the physical world? We offer a Markovian formulation of structured world models and outline the notion of multi-level hybrid world representations that support compositional structure. We then review and suggest possible discovery paradigms, spanning distillation, interaction-driven continual learning, and ensemble learning.

Jennifer M. Reddig, Scott Moon, Kaitlyn Crutcher, Christopher J. MacLellan

As artificial intelligence (AI) becomes increasingly integrated into daily life, higher education must move beyond code-centric instruction to foster holistic AI literacy. We present a novel pedagogical approach that integrates embodied, unplugged activities into a university-level Introduction to AI course. Inspired by the effectiveness of CS Unplugged in K-12 education, our physical, collaborative activities gave students a first-person perspective on AI decision-making. Through interactive games modeling Search Algorithms, Markov Decision Processes, Q-learning, and Hidden Markov Models, students built an intuition for complex AI concepts and more easily transitioned to mathematical formalizations and code implementations. We present four unplugged AI activities, describe how to bridge from unplugged activities to plugged coding tasks, reflect on implementation challenges, and propose refinements. We suggest that unplugged activities can effectively bridge conceptual reasoning and technical skill-building in university-level AI education.

Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals' daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals' daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key domains: physical, daily activity, cognitive engagement, and sleep patterns. Based on a longitudinal study of over one hundred participants, deployed with non-intrusive sensors in their homes to undergo parallel clinical evaluation across a period of five years, our model successfully identified individuals with MCI, achieving high predictive accuracy regardless the noisy and sparse availability of data. As a transparent neural network, the learned model can also be interpreted as classification rules to distinguish MCI from normal cognition (NC) cases based on the digital biomarkers. These results demonstrate that passively collected, sensor-derived digital biomarkers can be leveraged to indicate cognitive status and potentially providing clinically meaningful insights on the impairment conditions. We also discuss the practical challenges and lessons learned from this real-world deployment to inform future large-scale implementations of such AI-driven health monitoring systems.

Wenjie Lin, Jin Wei-Kocsis

While large language models (LLMs) have shown promise in healthcare, their application for rare medical conditions is still hindered by scarce and unreliable datasets for fine-tuning. Hyperhidrosis, a disorder causing excessive sweating beyond physiological needs, is one such rare disorder, affecting 2-3% of the population and significantly impacting both physical comfort and psychosocial well-being. To date, no work has tailored LLMs to advance the diagnosis or care of hyperhidrosis. To address this gap, we present LLM4Sweat, an open-source and domain-specific LLM framework for trustworthy and empathetic hyperhidrosis support. The system follows a three-stage pipeline. In the data augmentation stage, a frontier LLM generates medically plausible synthetic vignettes from curated open-source data to create a diverse and balanced question–answer dataset. In the fine-tuning stage, an open-source foundation model is fine-tuned on the dataset to provide diagnosis, personalized treatment recommendations, and empathetic psychological support. In the inference and expert evaluation stage, clinical and psychological specialists assess accuracy, appropriateness, and empathy, with validated responses iteratively enriching the dataset. Experiments show that LLM4Sweat outperforms baselines and delivers the first open-source LLM framework for hyperhidrosis, offering a generalizable approach for other rare diseases with similar data and trustworthiness challenges.

Qiming Guo, Bishal Khatri, Wenbo Sun, Jinwen Tang, Hua Zhang, Wenlu Wang

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Azza Fadhel, Nathaniel W. Zuckschwerdt, Aryan Deshwal, Susmita Bose, Amit Bandyopadhyay, Jana Doppa

Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in terms of resources (physical and human labor) and the configuration space is very large. This paper combines the general principles of AI-driven adaptive experimental design with domain knowledge to address the challenging problem of discovering feasible configurations. The key idea is to build a surrogate model from past experiments to intelligently select a small batch of input configurations for validation in each iteration. To demonstrate the effectiveness of this methodology, we deploy it for Directed Energy Deposition process to print GRCop-42, a high-performance copper–chromium–niobium alloy developed by NASA for aerospace applications. Within three months, our approach yielded multiple defect-free outputs across a range of laser powers—dramatically reducing time-to-result and resource expenditure compared to several months of manual experimentation by domain scientists with no success. By enabling high-quality GRCop-42 fabrication on readily available infrared laser platforms for the first time, we democratize access to this critical alloy, paving the way for cost-effective, decentralized production for aerospace applications.

Patrick Rodler, Birgit Hofer, Dietmar Jannach, Iulia Nica, Franz Wotawa

Model-based diagnosis is a generally applicable, principled approach to the systematic debugging of a wide range of system types such as circuits, knowledge bases, physical devices, or software. Based on a formal description of the system, it enables precise and deterministic reasoning about potential faults responsible for observed misbehavior. In software, such a formal system description can often even be extracted from the buggy program fully automatically. As logical reasoning is central to diagnosis, the performance of model-based debuggers is largely influenced by reasoning efficiency, which in turn depends on the complexity and expressivity of the system description. Since highly detailed models capturing exact semantics often exceed the capabilities of current reasoning tools, researchers have proposed more abstract representations. In this work, we thoroughly analyze system modeling techniques with a focus on fault localization in spreadsheets—one of the most widely used end-user programming paradigms. Specifically, we present three constraint model types characterizing spreadsheets at different abstraction levels, show how to extract them automatically from faulty spreadsheets, and provide theoretical and empirical investigations of the impact of abstraction on both diagnostic output and computational performance. Our main conclusions are that (i) for the model types, there is a trade-off between the conciseness of generated fault candidates and computation time, (ii) the exact model is often impractical, and (iii) a new model based on qualitative reasoning yields the same solutions as the exact one in up to more than half the cases while being orders of magnitude faster. Due to their ability to restrict the solution space in a sound way, the explored model-based techniques, rather than being used as standalone approaches, are expected to realize their full potential in combination with iterative sequential diagnosis or indeterministic but more performant statistical debugging methods.

Robert McCarthy, Daniel C.H. Tan, Dominik Schmidt, Fernando Acero, Nathan Herr, Yilun Du, Thomas G. Thuruthel, Zhibin Li

Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing. Robot learning, however, has thus far failed to replicate this success and remains constrained by a scarcity of available data. Learning from Videos (LfV) methods aim to address this data bottleneck by augmenting traditional robot data with large-scale internet video. This video data provides foundational information regarding physical dynamics, behaviours, and tasks, and can be highly informative for general-purpose robots. This survey systematically examines the emerging field of LfV. We first outline essential concepts, including detailing fundamental LfV challenges such as distribution shift and missing action labels in video data. Next, we comprehensively review current methods for extracting knowledge from large-scale internet video, overcoming LfV challenges, and improving robot learning through video-informed training. The survey concludes with a critical discussion of future opportunities. Here, we emphasize the need for scalable foundation model approaches that can leverage the full range of available internet video and enhance the learning of robot policies and dynamics models. Overall, the survey aims to inform and catalyse future LfV research, driving progress towards general-purpose robots.

Elena-Sorina Lupu, Fengze Xie, James Preiss, Jedidiah Alindogan, Matthew Anderson, Soon-Jo Chung

Control of off-road vehicles is challenging due to the complex dynamic interactions with the terrain. Accurate modeling of these interactions is important to optimize driving performance, but the relevant physical phenomena, such as slip, are too complex to model from first principles. Therefore, we present an offline meta-learning algorithm to construct a rapidly-tunable model of residual dynamics and disturbances. Our model processes terrain images into features using a visual foundation model (VFM), then maps these features and the vehicle state to an estimate of the current actuation matrix using a deep neural network (DNN). We then combine this model with composite adaptive control to modify the last layer of the DNN in real time, accounting for the remaining terrain interactions not captured during offline training. We provide mathematical guarantees of stability and robustness for our controller, and demonstrate the effectiveness of our method through simulations and hardware experiments with a tracked vehicle and a car-like robot. We evaluate our method outdoors on different slopes with varying slippage and actuator degradation disturbances, and compare against an adaptive controller that does not use the VFM terrain features. We show significant improvement over the baseline in both hardware experimentation and simulation.

Ruihua Han, Shuai Wang, Shuaijun Wang, Zeqing Zhang, Jianjun Chen, Shijie Lin, Chengyang Li, Chengzhong Xu, Yonina C. Eldar, Qi Hao 等

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via back propagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

Chen Wei

This talk surveys my research agenda on advancing general-purpose visual intelligence, moving AI beyond static recognition toward active reasoning and embodied action. A central challenge is enabling AI systems to generalize reliably in low-data and long-tail regimes. I address this by combining multimodal representation learning with agentic reasoning frameworks such as PyVision, which equips vision models to dynamically generate tools for deliberate problem-solving, and ViGaL, which leverages gameplay to instill transferable cognitive skills for reasoning under scarcity. These efforts chart a trajectory from representation and generation to interactive, embodied agents, re-imagining AI as an active collaborator capable of tool use, imagination, and purposeful engagement across both digital and physical environments.

Alexander Rodríguez

AI and population health are becoming increasingly intertwined, driven by the growing availability of multimodal data and rapid advances in AI. At the AAAI-26 New Faculty Highlights, I present our efforts to harness these trends to enhance our capacity to model, simulate, and adapt to complex dynamical processes. I first introduce our robust deep learning architectures for real-time outbreak response, highlighting how our frameworks capture uncertainty and dynamics across shifting distributions, multimodal data, hierarchical structures, and relational dependencies. I will then introduce our hybrid approaches that integrate machine learning with science-based mechanistic epidemiological models, including physics-informed neural networks, expert-guided generative models for causal inference, and differentiable agent-based models. Together, these advances illustrate how combining data-driven AI with domain knowledge can enable more reliable, adaptive, and actionable solutions to inform decision making in population health.