For a receding-horizon controller with a known system and with an approximate terminal value function, it is well-known that increasing the prediction horizon can improve its control performance. However, when the prediction model is inexact, a larger prediction horizon also causes propagation and accumulation of the prediction error. In this work, we aim to analyze the effect of the above trade-off between the modeling error, the terminal value function error, and the prediction horizon on the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a performance upper bound is obtained and suggests that for many cases, the prediction horizon should be either 1 or infinity to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error. The obtained suboptimality performance bound is also applied to provide end-to-end performance guarantees, e.g., regret bounds, for nominal receding-horizon LQ controllers in a learning-based setting.

Computer science advances in the last decades led us to experience in a relatively short lapse of time three major technological innovations: the personal computer, the Internet, and mobile devices. Currently, we are at the beginning of a fourth paradigm of computing innovations involving immersive technologies such as Virtual Reality (VR), Augmented Reality (AR) or Mixed Reality (MR). All this is possible due to huge advances in machine learning techniques and hardware improvements applied to computer graphics and computer vision. It is clear that this new paradigm seeks to revolutionize technology in the next years and will have a great impact in society such as smart cities [1, 2], new teaching methods [3] or economic paradigms [4]. Technology companies have already created numerous digital platforms, such as the Metaverse [5, 6] or the Omniverse [7, 8], in order to develop their own immersive technologies.

Gaussian process regression in its most simplified form assumes normal homoscedastic noise and utilizes analytically tractable mean and covariance functions of predictive posterior distribution using Gaussian conditioning. Its hyperparameters are estimated by maximizing the evidence, commonly known as type II maximum likelihood estimation. Unfortunately, Bayesian inference based on Gaussian likelihood is not robust to outliers, which are often present in the observational training data sets. To overcome this problem, we propose a robust process model in the Gaussian process framework with the likelihood of observed data expressed as the Huber probability distribution. The proposed model employs weights based on projection statistics to scale residuals and bound the influence of vertical outliers and bad leverage points on the latent functions estimates while exhibiting a high statistical efficiency at the Gaussian and thick tailed noise distributions. The proposed method is demonstrated by two real world problems and two numerical examples using datasets with additive errors following thick tailed distributions such as Students t, Laplace, and Cauchy distribution.

This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on the unstable dynamics to stabilize systems. This means it is sufficient to learn the unstable dynamics alone, which are typically confined to much lower dimensional spaces than the high-dimensional state spaces of all system dynamics and thus few data samples are sufficient to identify them. Numerical experiments demonstrate that context-aware controller inference learns stabilizing controllers from orders of magnitude fewer data samples than traditional data-driven control techniques and variants of reinforcement learning. The experiments further show that the low data requirements of context-aware controller inference are especially beneficial in data-scarce engineering problems with complex physics, for which learning complete system dynamics is often intractable in terms of data and training costs.

With modern computational advancements and statistical analysis methods, machine learning algorithms have become a vital part of engineering modeling. Neural Operator Networks (ONets) is an emerging machine learning algorithm as a "faster surrogate" for approximating solutions to partial differential equations (PDEs) due to their ability to approximate mathematical operators versus the direct approximation of Neural Networks (NN). ONets use the Universal Approximation Theorem to map finite-dimensional inputs to infinite-dimensional space using the branch-trunk architecture, which encodes domain and feature information separately before using a dot product to combine the information. ONets are expected to occupy a vital niche for surrogate modeling in physical systems and Digital Twin (DT) development. Three test cases are evaluated using ONets for operator approximation, including a 1-dimensional ordinary differential equations (ODE), general diffusion system, and convection-diffusion (Burger) system. Solutions for ODE and diffusion systems yield accurate and reliable results (R2>0.95), while solutions for Burger systems need further refinement in the ONet algorithm.

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are three-fold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws of the vehicle engine into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

Our researchers have created a new AI model that predicts protein folding 60x faster (!) than current state of the art. The potential is mind-blowingly exciting. With more than 600 million structures, the database is three times larger than anything before. To support and accelerate scientific research around the world, we are sharing our models, the research paper, the database, and an API that allows scientists to easily retrieve specific protein structures. AI research like this has incredible potential to help us identify new ways to treat disease, accelerate drug discovery, and even push forward clean energy research.

An experimental technique is presented to quantify the lateral restraint provided by a geogrid embedded in granular soil at the particle level. Repeated load triaxial tests were done on transparent sand specimens with geosynthetic inclusions simulating geogrids. Particle outlines on laser illuminated planes through the specimens were segmented using a deep learning-based segmentation algorithm. The particle outlines were characterized in terms of Fourier shape descriptors and tracked across sequentially captured images. The accuracy of the particle displacement measurements was validated against Digital Image Correlation (DIC) measurements. In addition, the method's resolution and repeatability is presented. Based on the measured particle displacements and rotations, a state boundary line between probable and improbable particle motions was identified for each test. The size of the zone of probable motions could be used to quantify the lateral restraint provided by the inclusions. Overall, the tests results revealed that the geosynthetic inclusions restricted both particle displacements and rotations. However, the particle displacements were found to be restrained more significantly than the rotations. Finally, a unique relationship was found between the magnitude of the permanent strains of the specimens and the size of the zone of probable motions.

Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least Squares or PLS ) or variable selections (e.g. lasso). Sparse partial least squares combine both strategies, by blending variable selection into PLS. The variant presented in this paper, Dual-sPLS, generalizes the classical PLS1 algorithm. It provides balance between accurate prediction and efficient interpretation. It is based on penalizations inspired by classical regression methods (lasso, group lasso, least squares, ridge) and uses the dual norm notion. The resulting sparsity is enforced by an intuitive shrinking ratio parameter. Dual-sPLS favorably compares to similar regression methods, on simulated and real chemical data. Code is provided as an open-source package in R: \url{https://CRAN.R-project.org/package=dual.spls}.

Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Because machine learning model predictions depend on the quality of the available training data, different domains of the material feature space are predicted with different accuracy levels by such models. The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors. In this work, we propose a method to find domains of applicability using a large feature space and also introduce analysis techniques to gain more insight into the detected domains and subdomains.