Related papers: Goal-Oriented Real-Time Bayesian Inference for Lin…
Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard because it alleviates ill-posedness issues and quantifies…
In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k.a. digital twin for anti-counterfeiting applications based on copy detection patterns (CDP). The digital twin is…
Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we…
Digital twins of complex physical systems are expected to infer unobserved states from sparse measurements and predict their evolution in time, yet these two functions are typically treated as separate tasks. Here we present GLU, a…
Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy density and non-stationary conditions. Wave energy converters…
Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled by mechanistic simulators whose parameters cannot be measured directly. In such…
Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs…
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a…
This work presents a physics-informed deep learning-based super-resolution framework to enhance the spatio-temporal resolution of the solution of time-dependent partial differential equations (PDE). Prior works on deep learning-based…
The dynamic nature of human health and comfort calls for adaptive systems that respond to individual physiological needs in real time. This paper presents an AI-enhanced digital twin framework that integrates biometric signals, specifically…
Digital twins (DTs), virtual simulated replicas of physical scenes, are transforming various industries. However, their potential in radio frequency (RF) sensing applications has been limited by the unidirectional nature of conventional RF…
With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the…
The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing radiation, enabling improved understanding of atmospheric…
The adoption of cyber-physical systems and jobsite intelligence that connects design models, real-time site sensing, and autonomous field operations can dramatically enhance digital management in the construction industry. This paper…
This paper presents a deep learning-based method for dynamic gear measurement and uncertainty estimation. A twin-system proposed on the Unity platform is utilized to flexibly generate diverse simulated datasets. This effectively addresses…
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational…
In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation codes are necessary. The increased complexity of models…
By offering a dynamic, real-time virtual representation of physical systems, digital twin technology can enhance data-driven decision-making in digital agriculture. Our research shows how digital twins are useful for detecting…
The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains…
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We…