Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing non-additive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressible as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it performs better than or on par with state-of-the-art methods in batched Bayesian optimization.

Accurate forest canopy height estimation is essential for evaluating aboveground biomass and carbon stock dynamics, supporting ecosystem monitoring services like timber provisioning, climate change mitigation, and biodiversity conservation. However, despite advancements in spaceborne LiDAR technology, data for northern high latitudes remain limited due to orbital and sampling constraints. This study introduces a methodology for generating spatially continuous, high-resolution canopy height and uncertainty estimates using Deep Learning Regression models. We integrate multi-source, multi-seasonal satellite data from Sentinel-1, Landsat, and ALOS-PALSAR-2, with spaceborne GEDI LiDAR as reference data. Our approach was tested in Ontario, Canada, and validated with airborne LiDAR, demonstrating strong performance. The best results were achieved by incorporating seasonal Sentinel-1 and Landsat features alongside PALSAR data, yielding an R-square of 0.72, RMSE of 3.43 m, and bias of 2.44 m. Using seasonal data instead of summer-only data improved variability by 10%, reduced error by 0.45 m, and decreased bias by 1 m. The deep learning model's weighting strategy notably reduced errors in tall canopy height estimates compared to a recent global model, though it overestimated lower canopy heights. Uncertainty maps highlighted greater uncertainty near forest edges, where GEDI measurements are prone to errors and SAR data may encounter backscatter issues like foreshortening, layover, and shadow. This study enhances canopy height estimation techniques in areas lacking spaceborne LiDAR coverage, providing essential tools for forestry, environmental monitoring, and carbon stock estimation.

Effective monitoring of freight transportation is essential for advancing sustainable, low-carbon economies. Traditional methods relying on single-modal data and discrete simulations fall short in optimizing intermodal systems holistically. These systems involve interconnected processes that affect shipping time, costs, emissions, and socio-economic factors. Developing digital twins for real-time awareness, predictive analytics, and urban logistics optimization requires extensive efforts in knowledge discovery, data integration, and multi-domain simulation. Recent advancements in generative AI offer new opportunities to streamline digital twin development by automating knowledge discovery and data integration, generating innovative simulation and optimization solutions. These models extend digital twins' capabilities by promoting autonomous workflows for data engineering, analytics, and software development. This paper proposes an innovative paradigm that leverages generative AI to enhance digital twins for urban research and operations. Using freight decarbonization as a case study, we propose a conceptual framework employing transformer-based language models to enhance an urban digital twin through foundation models. We share preliminary results and our vision for more intelligent, autonomous, and general-purpose digital twins for optimizing integrated freight systems from multimodal to synchromodal paradigms.

Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic information about the objects of interest from raw sensory data. While deep learning-based (DL) methods excel at inferring semantic object information from images, such as class and relative 6 degree of freedom (6-DoF) pose, they are computationally demanding and thus often not suitable for payload constrained mobile robots. In this letter we present a real-time capable unmanned aerial vehicle (UAV) system for object-relative, closed-loop navigation with a minimal sensor configuration consisting of an inertial measurement unit (IMU) and RGB camera. Utilizing a DL-based object pose estimator, solely trained on synthetic data and optimized for companion board deployment, the object-relative pose measurements are fused with the IMU data to perform object-relative localization. We conduct multiple real-world experiments to validate the performance of our system for the challenging use case of power pole inspection. An example closed-loop flight is presented in the supplementary video.

Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, often fail to uncover valuable insights not explicitly stated in article titles or keywords. These approaches are unable to perform semantic searches and contextual understanding, limiting their effectiveness in classifying topics and characterizing studies. In this paper, we address these limitations by leveraging Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis. We developed a technical workflow that integrates a vector database, Sentence Transformers, a Gaussian Mixture Model (GMM), Retrieval Agent, and Large Language Models (LLMs) to enable contextual search, topic ranking, and characterization of research using customized prompt templates. A pilot study analyzing 223 urban science-related articles published in Nature Communications over the past decade highlights the effectiveness of our approach in generating insightful summary statistics on the quality, scope, and characteristics of papers in high-impact journals. This study introduces a new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.

Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques. Motivated by this, we explore whether a similar approach can be applied to scientific foundation models (SFMs). Our methodology is structured as follows: (i) we collect low-cost physics-informed neural network (PINN)-based approximated prior data in the form of solutions to partial differential equations (PDEs) constructed through an arbitrary linear combination of mathematical dictionaries; (ii) we utilize Transformer architectures with self and cross-attention mechanisms to predict PDE solutions without knowledge of the governing equations in a zero-shot setting; (iii) we provide experimental evidence on the one-dimensional convection-diffusion-reaction equation, which demonstrate that pre-training remains robust even with approximated prior data, with only marginal impacts on test accuracy. Notably, this finding opens the path to pre-training SFMs with realistic, low-cost data instead of (or in conjunction with) numerical high-cost data. These results support the conjecture that SFMs can improve in a manner similar to LLMs, where fully cleaning the vast set of sentences crawled from the Internet is nearly impossible. In developing large-scale models, one fundamental challenge is the inherent noisiness of the data used for training. Whether dealing with natural language, scientific data, or other domains, large datasets almost inevitably contain noise. Large language models (LLMs), such as ChatGPT, present an interesting paradox: despite being trained on noisy datasets, they consistently produce remarkably clean and coherent output.

From common-sense reasoning to domain-specific tasks, parameter-efficient fine tuning (PEFT) methods for large language models (LLMs) have showcased significant performance improvements on downstream tasks. However, fine-tuned LLMs often struggle with overconfidence in uncertain predictions, particularly due to sparse training data. This overconfidence reflects poor epistemic uncertainty calibration, which arises from limitations in the model's ability to generalize with limited data. Existing PEFT uncertainty quantification methods for LLMs focus on the post fine-tuning stage and thus have limited capability in calibrating epistemic uncertainty. To address these limitations, we propose Functional-Level Uncertainty Quantification for Calibrated Fine-Tuning (UQ4CT), which captures and calibrates functional-level epistemic uncertainty during the fine-tuning stage via a mixture-of-expert framework. We show that UQ4CT reduces Expected Calibration Error (ECE) by more than $25\%$ while maintaining high accuracy across $5$ benchmarks. Furthermore, UQ4CT maintains superior ECE performance with high accuracy under distribution shift, showcasing improved generalizability.

Lithium-ion batteries are a key energy storage technology driving revolutions in mobile electronics, electric vehicles and renewable energy storage. Capacity retention is a vital performance measure that is frequently utilized to assess whether these batteries have approached their end-of-life. Machine learning (ML) offers a powerful tool for predicting capacity degradation based on past data, and, potentially, prior physical knowledge, but the degree to which an ML prediction can be trusted is of significant practical importance in situations where consequential decisions must be made based on battery state of health. This study explores the efficacy of fully Bayesian machine learning in forecasting battery health with the quantification of uncertainty in its predictions. Specifically, we implemented three probabilistic ML approaches and evaluated the accuracy of their predictions and uncertainty estimates: a standard Gaussian process (GP), a structured Gaussian process (sGP), and a fully Bayesian neural network (BNN). In typical applications of GP and sGP, their hyperparameters are learned from a single sample while, in contrast, BNNs are typically pre-trained on an existing dataset to learn the weight distributions before being used for inference. This difference in methodology gives the BNN an advantage in learning global trends in a dataset and makes BNNs a good choice when training data is available. However, we show that pre-training can also be leveraged for GP and sGP approaches to learn the prior distributions of the hyperparameters and that in the case of the pre-trained sGP, similar accuracy and improved uncertainty estimation compared to the BNN can be achieved. This approach offers a framework for a broad range of probabilistic machine learning scenarios where past data is available and can be used to learn priors for (hyper)parameters of probabilistic ML models.

A machine-learnable variational scheme using Gaussian radial basis functions (GRBFs) is presented and used to approximate linear problems on bounded and unbounded domains. In contrast to standard mesh-free methods, which use GRBFs to discretize strong-form differential equations, this work exploits the relationship between integrals of GRBFs, their derivatives, and polynomial moments to produce exact quadrature formulae which enable weak-form expressions. Combined with trainable GRBF means and covariances, this leads to a flexible, generalized Galerkin variational framework which is applied in the infinite-domain setting where the scheme is conforming, as well as the bounded-domain setting where it is not. Error rates for the proposed GRBF scheme are derived in each case, and examples are presented demonstrating utility of this approach as a surrogate modeling technique.

Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70\% improvement over other work on Molecular Dynamics data.