arXiv subjects

Craig Michoski

Publications and source records attributed to Craig Michoski.

14 recordsLinked to original sources

arXiv2026

MGKDB: An IMAS-aligned multicode gyrokinetic simulation database for reproducible fusion turbulence modeling and data-driven analysis

Expensive fusion simulations are commonly preserved in code-specific formats that limit discovery, comparison, and reuse. We present the Multiscale GyroKinetic DataBase (MGKDB), an open-source software framework and curated archive that converts heterogeneous simulation campaigns into traceable scientific records. Each record links code-native inputs and outputs to provenance and quality metadata, an IMAS-aligned physics representation, and derived diagnostics, preserving model-specific evidence while enabling common-field queries. Production pathways support linear and nonlinear GENE and CGYRO calculations and reduced quasilinear TGLF evaluations. At the September 1, 2026 snapshot, MGKDB contained 1,068,089 records, nearly all of which included a populated gyrokinetics IMAS branch. The software is openly available, while access to the NERSC-hosted production records is managed. Three demonstrations show how these linked representations support scientific reuse. Standardized quantities stored in the Diagnostics branch enable population-scale analysis of archived linear modes; common input coordinates reveal coverage, redundancy, and campaign-driven sampling structure across a multicode collection; and record-level retrieval of native CGYRO inputs drives matched TGLF calculations and produces a traceable dataset for exploratory surrogate modeling. Together, these examples demonstrate how MGKDB supports archive characterization, candidate cross-code and cross-fidelity comparisons, campaign planning, and reproducible data-driven modeling without treating different models as automatically equivalent.

physics.plasm-ph↗

arXiv2026

The Fusion Equilibrium Challenge: Inferring Magnetic Geometry Without Magnetic Diagnostics

Next-generation fusion reactor devices such as SPARC, ARC, and CFETR will operate in extreme neutron environments that compromise the magnetic sensors traditionally used to reconstruct plasma equilibria. However, reliable knowledge of the plasma equilibrium--including magnetic flux surfaces, safety factor profiles, and shaping parameters--is indispensable for real-time control, disruption avoidance, and physics interpretation. The Fusion Equilibrium Challenge invites the NeurIPS community to confront a deceptively simple but scientifically rigorous inverse problem: reconstruct the two-dimensional poloidal flux function psi(R,Z) and a suite of scalar equilibrium parameters from non-magnetic diagnostics alone, namely external poloidal-field coil currents and Thomson-scattering electron temperature/density profiles. The challenge provides the first open-access, harmonized multi-machine benchmark for fusion, releasing a curated dataset of 9,113 DIII-D shots and 2,416 MAST shots--filtered for Thomson-diagnostic availability, feature completeness, and EFIT-reconstruction quality. Each shot is packaged into a standard Parquet file containing approximately 260 (DIII-D) / approximately 80 (MAST) EFIT flux maps and rich high-rate diagnostics. Two complementary awards reward intra-machine reconstruction fidelity (S_model) on DIII-D and zero-shot cross-machine generalization (G_ratio) to the topologically distinct MAST spherical tokamak. We argue that the challenge functions as a benchmark for reactor-ready equilibrium inference and as a probe of how far machine learning can be pushed toward truly machine-agnostic plasma state estimation.

physics.plasm-ph↗

arXiv2025

The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy

Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these datasets demand the development of new tools capable of systematically harmonizing and extracting knowledge across diverse modalities. The Data Fusion Labeler (dFL) is introduced as a unified workflow instrument that performs uncertainty-aware data harmonization, schema-compliant data fusion, and provenance-rich manual and automated labeling at scale. By embedding alignment, normalization, and labeling within a reproducible, operator-order-aware framework, dFL reduces time-to-analysis by greater than 50X (e.g., enabling >200 shots/hour to be consistently labeled rather than a handful per day), enhances label (and subsequently training) quality, and enables cross-device comparability. Case studies from DIII-D demonstrate its application to automated ELM detection and confinement regime classification, illustrating its potential as a core component of data-driven discovery, model validation, and real-time control in future burning plasma devices.

physics.plasm-ph↗

arXiv2025

Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition

In this work, we address the often-overlooked issue of Timescale Dependent Label Inconsistency (TsDLI) in training neural network models for EEG-based human emotion recognition. To mitigate TsDLI and enhance model generalization and explainability, we propose two novel regularization strategies: Local Variation Loss (LVL) and Local-Global Consistency Loss (LGCL). Both methods incorporate classical mathematical principles--specifically, functions of bounded variation and commute-time distances--within a graph theoretic framework. Complementing our regularizers, we introduce a suite of new evaluation metrics that better capture the alignment between temporally local predictions and their associated global emotion labels. We validate our approach through comprehensive experiments on two widely used EEG emotion datasets, DREAMER and DEAP, across a range of neural architectures including LSTM and transformer-based models. Performance is assessed using five distinct metrics encompassing both quantitative accuracy and qualitative consistency. Results consistently show that our proposed methods outperform state-of-the-art baselines, delivering superior aggregate performance and offering a principled trade-off between interpretability and predictive power under label inconsistency. Notably, LVL achieves the best aggregate rank across all benchmarked backbones and metrics, while LGCL frequently ranks the second, highlighting the effectiveness of our framework.

cs.CV↗

arXiv2025

Quantifying Resolution Limits in Pedestal Profile Measurements with Gaussian Process Regression

Edge transport barriers (ETBs) in magnetically confined fusion plasmas, commonly known as pedestals, play a crucial role in achieving high confinement plasmas. However, their defining characteristic, a steep rise in plasma pressure over short length scales, makes them challenging to diagnose experimentally. In this work, we use Gaussian Process Regression (GPR) to develop first-principles metrics for quantifying the spatiotemporal resolution limits of inferring differentiable profiles of temperature, pressure, or other quantities from experimental measurements. Although we focus on pedestals, the methods are fully general and can be applied to any setting involving the inference of profiles from discrete measurements. First, we establish a correspondence between GPR and low-pass filtering, giving an explicit expression for the effective `cutoff frequency' associated with smoothing incurred by GPR. Second, we introduce a novel information-theoretic metric, \(N_{eff}\), which measures the effective number of data points contributing to the inferred value of a profile or its derivative. These metrics enable a quantitative assessment of the trade-off between `over-fitting' and `over-regularization', providing both practitioners and consumers of GPR with a systematic way to evaluate the credibility of inferred profiles. We apply these tools to develop practical advice for using GPR in both time-independent and time-dependent settings, and demonstrate their usage on inferring pedestal profiles using measurements from the DIII-D tokamak.

physics.plasm-ph↗

arXiv2024

Emotion Classification from Multi-Channel EEG Signals Using HiSTN: A Hierarchical Graph-based Spatial-Temporal Approach

This study introduces a parameter-efficient Hierarchical Spatial Temporal Network (HiSTN) specifically designed for the task of emotion classification using multi-channel electroencephalogram data. The network incorporates a graph hierarchy constructed from bottom-up at various abstraction levels, offering the dual advantages of enhanced task-relevant deep feature extraction and a lightweight design. The model's effectiveness is further amplified when used in conjunction with a proposed unique label smoothing method. Comprehensive benchmark experiments reveal that this combined approach yields high, balanced performance in terms of both quantitative and qualitative predictions. HiSTN, which has approximately 1,000 parameters, achieves mean F1 scores of 96.82% (valence) and 95.62% (arousal) in subject-dependent tests on the rarely-utilized 5-classification task problem from the DREAMER dataset. In the subject-independent settings, the same model yields mean F1 scores of 78.34% for valence and 81.59% for arousal. The adoption of the Sequential Top-2 Hit Rate (Seq2HR) metric highlights the significant enhancements in terms of the balance between model's quantitative and qualitative for predictions achieved through our approach when compared to training with regular one-hot labels. These improvements surpass 50% in subject-dependent tasks and 30% in subject-independent tasks. The study also includes relevant ablation studies and case explorations to further elucidate the workings of the proposed model and enhance its interpretability.

eess.SP↗

arXiv2023

Automated Estimation of Plasma Temperature and Density from Emission Spectroscopy

This paper introduces a novel approach for automated estimation of plasma temperature and density using emission spectroscopy, integrating Bayesian inference with sophisticated physical models. We provide an in-depth examination of Bayesian methods applied to the complexities of plasma diagnostics, supported by a robust framework of physical and measurement models. Our methodology is validated through experimental observations, focusing on individual and sequential shot analyses. The results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of plasma parameter estimation, marking a significant advancement in the field of emission spectroscopy for plasma diagnostics. This study not only offers a new perspective in plasma analysis but also paves the way for further research and applications in nuclear instrumentation and related domains.

physics.plasm-ph↗

arXiv2022

A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data

In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features' Gram matrices into attention matrices for better feature refinement. Our experiments have shown that MCAM's effectiveness is comparable to state-of-the-art attention modules in boosting the backbone network's performance in prediction while requiring less parameters. Several accompanying sensitivity analyses on trained models' prediction concerning different attacks are also performed. These attacks include various frequency domain filtering levels and gradually morphing between samples associated with multiple labels. Our results can help better understand different modules' behaviour in prediction and can provide guidance in applications where data is limited and are with noises.

eess.SP↗

arXiv2022

KAM -- a Kernel Attention Module for Emotion Classification with EEG Data

In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks. The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitatively examining the amount of attention assigned during deep feature refinement, hence help better interpret a trained model. Using EEGNet as the backbone model, extensive experiments are conducted on the SEED dataset to assess the module's performance on within-subject classification tasks compared to other SOTA attention modules. Requiring only one extra parameter, the inserted module is shown to boost the base model's mean prediction accuracy up to more than 1\% across 15 subjects. A key component of the method is the interpretability of solutions, which is addressed using several different techniques, and is included throughout as part of the dependency analysis.

cs.CV↗

arXiv2022

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

physics.plasm-ph↗

arXiv2022

Reduced models for ETG transport in the pedestal

This paper reports on the development of reduced models for electron temperature gradient (ETG) driven transport in the pedestal. Model development is enabled by a set of 61 nonlinear gyrokinetic simulations with input parameters taken from the pedestals in a broad range of experimental scenarios. The simulation data has been consolidated in a new database for gyrokinetic simulation data, the Multiscale Gyrokinetic Database (MGKDB), facilitating the analysis. The modeling approach may be considered a generalization of the standard quasilinear mixing length procedure. The parameter eta, the ratio of the density to temperature gradient scale length, emerges as the key parameter for formulating an effective saturation rule. With a single order-unity fitting coefficient, the model achieves an RMS error of 15%. A similar model for ETG particle flux is also described. We also present simple algebraic expressions for the transport informed by an algorithm for symbolic regression.

physics.plasm-ph↗

arXiv2020

Moment Preserving Constrained Resampling with Applications to Particle-in-Cell Methods

In simulations of partial differential equations using particle-in-cell (PIC) methods, it is often advantageous to resample the particle distribution function to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. We introduce an algorithm for particle resampling called Moment Preserving Contrained Resampling (MPCR). The general algorithm partitions the system space into smaller subsets and is designed to conserve any number of particle and grid quantities with a high degree of accuracy (i.e. machine accuracy). The resampling scheme can be integrated into any PIC code. The advantages of MPCR, including performance, accuracy, and stability, are presented by examining several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The tests demonstrate that while the computational cost of MPCR is negligible compared to the nascent particle evolution in PIC methods, periodic particle resampling yields a significant improvement in the accuracy and stability of the results.

physics.plasm-ph↗

arXiv2019

Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks

Recent work has introduced a simple numerical method for solving partial differential equations (PDEs) with deep neural networks (DNNs). This paper reviews and extends the method while applying it to analyze one of the most fundamental features in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to compressible Euler equations is discussed, analyzed, and then compared to conventional finite element and finite volume methods. These methods are extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is \emph{a priori} insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms and using supervised training on synthetic experimental data. The resulting DNN framework for PDEs seems to demonstrate almost fantastical ease of system prototyping, natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of the entire parameter space.

cs.LG↗

arXiv2018

FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning

Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/software stacks. FanStore distributes datasets to the local storage of compute nodes, and maintains a global namespace. With the techniques of system call interception, distributed metadata management, and generic data compression, FanStore provides a POSIX-compliant interface with native hardware throughput in an efficient and scalable manner. Users do not have to make intrusive code changes to use FanStore and take advantage of the optimized I/O. Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.

cs.DC↗