Understanding high energy density physics (HEDP) is critical for advancements in fusion energy and astrophysics. The computational demands of the computer models used for HEDP studies have led researchers to explore deep learning methods to enhance simulation efficiency. This paper introduces HEDP-Gen, a framework for training and evaluating generative models tailored for HEDP. Central to HEDP-Gen is Geom-WAE-a generalized Wasserstein auto-encoder accommodating both Euclidean and non-Euclidean latent spaces. HEDP-Gen establishes a rigorous evaluation standard, assessing not only reconstruction fidelity but also scientific validity, sample diversity, and latent space utility in geodesic interpolation and attribute traversal. A case study using hyperbolic geometry (Poincaréball prior) demonstrates that non-Euclidean priors yield scientifically valid samples and stronger generalization in downstream tasks, advantages often missed by conventional reconstruction metrics. High energy density physics (HEDP) is crucial for advancements in fusion energy and astrophysics, yet its simulations are complex and computationally demanding. The authors introduce HEDP-Gen, a deep learning framework which uses advanced geometry in model design, and show that it enhances simulation efficiency and produces scientifically accurate results.

The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly Non-Local Thermodynamic Equilibrium (NLTE) model with machine learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal component spaces compared to conventional transformations.

Agent-based models of malaria transmission are useful tools for understanding disease dynamics and planning interventions, but they can be computationally intensive to calibrate. We present a multitask deep learning approach for emulating and calibrating a complex agent-based model of malaria transmission. Our neural network emulator was trained on a large suite of simulations from the EMOD malaria model, an agent-based model of malaria transmission dynamics, capturing relationships between immunological parameters and epidemiological outcomes such as age-stratified incidence and prevalence across eight sub-Saharan African study sites. We then use the trained emulator in conjunction with parameter estimation techniques to calibrate the underlying model to reference data. Taken together, this analysis shows the potential of machine learning-guided emulator design for complex scientific processes and their comparison to field data.

Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extrapolation capabilities. In this paper, we systematically explore anchoring as a general protocol for training vision models, providing fundamental insights into its training and inference processes and their implications for generalization and safety. Despite its promise, we identify a critical problem in anchored training that can lead to an increased risk of learning undesirable shortcuts, thereby limiting its generalization capabilities. To address this, we introduce a new anchored training protocol that employs a simple regularizer to mitigate this issue and significantly enhances generalization. We empirically evaluate our proposed approach across datasets and architectures of varying scales and complexities, demonstrating substantial performance gains in generalization and safety metrics compared to the standard training protocol.

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

Anchoring is a recent architecture and task-agnostic technique that can produce state-of-the-art epistemic uncertainty estimates, and improve extrapolation capabilities. However, the differences between anchored models and non-anchored variants is not well studied -- as there is little insight into the kinds of functions anchoring induces and how they behave under distribution shifts. In this paper, we analyze and improve anchoring as a training protocol for deep neural networks, evaluating them on important tasks of out of distribution generalization, task adaptation, anomaly detection and calibration. We pinpoint the impact of anchoring on generalization as being inversely related to the sensitivity of the model to the distribution of residuals. We further improve this sensitivity using a new technique called Random Anchor Masking (RAM) that significantly improves the quality of anchored models. We build evidence for the superiority of RAM-training using a range of benchmarks of varying size, using neural networks of varying complexity and scale.

While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while \textit{post-hoc} calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better \textit{intrinsic} uncertainty estimates are particularly valuable, as whey can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the structure induced by our anchoring strategies renders this unnecessary and allows us to support \gduq~ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on other uncertainty-based tasks like out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates.

Safe deployment of AI models requires proactive detection of failures to prevent costly errors. To this end, we study the important problem of detecting failures in deep regression models. Existing approaches rely on epistemic uncertainty estimates or inconsistency w.r.t the training data to identify failure. Interestingly, we find that while uncertainties are necessary they are insufficient to accurately characterize failure in practice. Hence, we introduce PAGER (Principled Analysis of Generalization Errors in Regressors), a framework to systematically detect and characterize failures in deep regressors. Built upon the principle of anchored training in deep models, PAGER unifies both epistemic uncertainty and complementary manifold non-conformity scores to accurately organize samples into different risk regimes.

Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. This is an oversimplification of the process of novel category recognition, where different regions of the image may have properties from different seen classes and thus have different predominant attributes. With this in mind, we take a fundamentally different approach: a pre-trained Vision-Language detector (VINVL) sensitive to attribute information is employed to efficiently obtain region features. A learned function maps the region features to region-specific attribute attention used to construct class part prototypes. We conduct experiments on a popular GZSL benchmark consisting of the CUB, SUN, and AWA2 datasets where our proposed Part Prototype Network (PPN) achieves promising results when compared with other popular base models. Corresponding ablation studies and analysis show that our approach is highly practical and has a distinct advantage over global attribute attention when localized proposals are available.

Safe deployment of AI models requires proactive detection of potential prediction failures to prevent costly errors. While failure detection in classification problems has received significant attention, characterizing failure modes in regression tasks is more complicated and less explored. Existing approaches rely on epistemic uncertainties or feature inconsistency with the training distribution to characterize model risk. However, we show that uncertainties are necessary but insufficient to accurately characterize failure, owing to the various sources of error. In this paper, we propose PAGER (Principled Analysis of Generalization Errors in Regressors), a framework to systematically detect and characterize failures in deep regression models. Built upon the recently proposed idea of anchoring in deep models, PAGER unifies both epistemic uncertainties and novel, complementary non-conformity scores to organize samples into different risk regimes, thereby providing a comprehensive analysis of model errors. Additionally, we introduce novel metrics for evaluating failure detectors in regression tasks. We demonstrate the effectiveness of PAGER on synthetic and real-world benchmarks. Our results highlight the capability of PAGER to identify regions of accurate generalization and detect failure cases in out-of-distribution and out-of-support scenarios.

Safe deployment of AI systems requires models to accurately flag anomalous or semantically unrelated data, while also generalizing to unseen shifts in the data distribution. While both these problems have been extensively studied, there is a risk for undesirable trade-off when exclusively optimizing for one of the objectives. In this paper, we systematically study this trade-off under the lens of model anchoring. Anchoring is a recently proposed training methodology that involves reparameterizing input data into anchor-residual pairs (anchors are drawn from the training data itself), thus establishing a combinatorial relationship with other samples in the training data. We make a surprising finding that the dual objectives of generalization and anomaly detection can be controlled by independently regularizing the model’s dependency on the distribution of anchors and residuals respectively. This enables, for the first time, a finer control of the detection-generalization trade-off without requiring any additional data (e.g., outlier exposure) or computationally intensive modeling strategies (e.g., deep ensembling).

While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while post-hoc calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better intrinsic uncertainty estimates are particularly valuable, as they can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the functional diversity induced by our anchoring strategies when sampling hypotheses renders this unnecessary and allows us to support G-$\Delta$UQ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on the uncertainty-based tasks of out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates.

This work explores the challenges of leveraging large-scale vision language models, such as CLIP, for visual relationship prediction (VRP), a task vital in understanding the relations between objects in a scene based on both image features and text descriptors. Despite its potential, we find that CLIP’s language priors are restrictive in effectively differentiating between various predicates for VRP. Towards this, we present CREPE (CLIP Representation Enhanced Predicate Estimation), which utilizes learnable prompts and a unique contrastive training strategy to derive reliable CLIP representations suited for VRP. CREPE can be seamlessly integrated into any VRP method. Our evaluations on the Visual Genome benchmark illustrate that using representations from CREPE significantly enhances the performance of vanilla VRP methods, such as UVTransE and VCTree. This enhancement is notable as CREPE can be seamlessly integrated into any VRP method, even without the need for additional calibration techniques, showcasing its efficacy as a powerful solution to VRP. CREPE’s performance on the Unrel benchmark reveals strong generalization to diverse and previously unseen predicate occurrences, despite lacking explicit training on such examples.

We address the crucial task of developing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Calibration enables deep networks to protect against trivial decision rules and controls over-generalization, thereby supporting model reliability. Given the challenges involved in curating appropriate calibration datasets, synthetic augmentations have gained significant popularity for inlier/outlier specification. Despite the rapid progress in data augmentation techniques, our study reveals a remarkable finding: the synthesis space and augmentation type play a pivotal role in effectively calibrating OOD detectors. Using the popular energy-based OOD detection framework, we find that the optimal protocol is to synthesize latent-space inliers along with diverse pixel-space outliers. Through extensive empirical studies conducted on multiple medical imaging benchmarks, we consistently demonstrate the superiority of our approach, achieving substantial improvements of 15% - 35% in AUROC compared to the state-of-the-art across various open-set recognition settings.

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final Section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary Section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.

Time-series are commonly susceptible to various types of corruption due to sensor-level changes and defects which can result in missing samples, sensor and quantization noise, unknown calibration, unknown phase shifts etc. These corruptions cannot be easily corrected as the noise model may be unknown at the time of deployment. This also results in the inability to employ pre-trained classifiers, trained on (clean) source data. In this paper, we present a general framework and models for time-series that can make use of (unlabeled) test samples to estimate the noise model—entirely at test time. To this end, we use a coupled decoder model and an additional neural network which acts as a learned noise model simulator. We show that the framework is able to "clean" the data so as to match the source training data statistics and the cleaned data can be directly used with a pre-trained classifier for robust predictions. We perform empirical studies on diverse application domains with different types of sensors, clearly demonstrating the effectiveness and generality of this method.

Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, geometry-based alignment methods, e.g., Orthogonal Procrustes Alignment (OPA), formed an important class of solutions to this problem. Despite their mathematical tractability, they rarely produce effective adaptation performance with the recent benchmarks. Instead, state-of-the-art approaches rely on sophisticated distribution alignment strategies such as adversarial training. In this paper, we show that, conventional OPA, when coupled with powerful deep feature extractors and a novel bi-level optimization formulation, is indeed an effective choice for handling challenging distribution shifts. When compared to existing UDA methods, our approach offers the following benefits: computational efficiency: Through the isolation of alignment and classifier training steps during adaptation, and the use of deep OPA, our approach is computationally very effective (typically requiring only 700 K parameters more than the base feature extractor as compared to millions of extra parameters required by state-of-the-art UDA baselines); (ii) data efficiency: Our approach does not require updating our feature extractor during adaptation and hence can be effective even with limited target data; (iii) improved generalization: The resulting models are intrinsically well-regularized and demonstrate effective generalization even in the challenging partial DA setting, i.e., target domain contains only a subset of the classes observed in the source domain.; and (iv) incremental training: Our approach allows progressive adaptation of models to novel domains (unseen during training) without requiring retraining of the model from scratch.

The PROBIES diagnostic is a new, highly flexible, imaging and energy spectrometer designed for laser-accelerated protons. The diagnostic can detect low-mode spatial variations in the proton beam profile while resolving multiple energies on a single detector or more. When a radiochromic film stack is employed for “single-shot mode,” the energy resolution of the stack can be greatly increased while reducing the need for large numbers of films; for example, a recently deployed version allowed for 180 unique energy measurements spanning ∼3 to 75 MeV with <0.4 MeV resolution using just 20 films vs 180 for a comparable traditional film and filter stack. When utilized with a scintillator, the diagnostic can be run in high-rep-rate (>Hz rate) mode to recover nine proton energy bins. We also demonstrate a deep learning-based method to analyze data from synthetic PROBIES images with greater than 95% accuracy on sub-millisecond timescales and retrained with experimental data to analyze real-world images on sub-millisecond time-scales with comparable accuracy.

To be successful in single source domain generalization (SSDG), maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Recent success in SSDG comes from methods that pre-specify diversity inducing image augmentations during training, so that it may lead to better generalization on new domains. However, naïve pre-specified augmentations are not always effective, either because they cannot model large domain shift, or be-cause the specific choice of transforms may not cover the types of shift commonly occurring in domain generalization. To address this issue, we present a novel framework called ALT: adversarially learned transformations, that uses an adversary neural network to model plausible, yet hard image transformations that fool the classifier. ALT learns image transformations by randomly initializing the adversary net-work for each batch and optimizing it for a fixed number of steps to maximize classification error. The classifier is trained by enforcing a consistency between its predictions on the clean and transformed images. With extensive empirical analysis, we find that this new form of adversarial transformations achieves both objectives of diversity and hardness simultaneously, outperforming all existing techniques on competitive benchmarks for SSDG. We also show that ALT can seamlessly work with existing diversity modules to produce highly distinct, and large transformations of the source domain leading to state-of-the-art performance. Code: https://github.com/tejas-gokhale/ALT