arXiv subjects

Joseph Schmidt

Publications and source records attributed to Joseph Schmidt.

5 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

Microtearing Thresholds and Second-Stable Ballooning in the DIII-D Pedestal: Reduced Modeling and Core-Edge Implications

Global and local linear gyrokinetic simulations of 42 pedestal equilibria from three DIII-D discharges are used to investigate pedestal stability and its impact on pedestal structure and confinement. Microtearing modes (MTMs) and kinetic ballooning modes (KBMs) represent the main ion scale instabilities. For all three discharges, MTMs lie near a stability boundary in the mid-pedestal and exhibit threshold behavior, with growth rates increasing at and beyond pre-ELM pressure gradients. Pedestal MTMs retain conventional signatures but also show enhanced particle transport and partial density-gradient drive, indicating they can constrain pedestal {\it pressure} rather than electron temperature alone. KBMs are typically second-stable in this region due to low magnetic shear and large pressure gradients, though they can become active near the pedestal foot where magnetic shear is higher. These findings suggest MTMs play the role of inter-ELM pressure limit in the mid-pedestal when KBM is second stable. A preliminary quasilinear mixing-length transport model, with properly tuned free parameters, reproduces experimental temperature and density profiles when coupled to ASTRA. When applied to a case with doubled separatrix density, the model predicts reduced pedestal pressure consistent with ITPA H-mode confinement trends, attributable to increased MTM and ETG transport. These results clarify pedestal-limiting mechanisms and establish a physics-based link between separatrix conditions, pedestal structure, and global confinement. This work lays the foundation for new predictive modeling capabilities for core-edge integration in burning plasma regimes.

physics.plasm-ph↗

arXiv2025

Generative Molecular Design with Steerable and Granular Synthesizability Control

Designing molecules that are both property-optimal and readily synthesizable is a central challenge in drug discovery. Existing works that do consider synthesizability can jointly output predicted synthesis routes for generated molecules. However, there has been minimal attention in addressing the ease of synthesis and with flexibility to incorporate desired reaction constraints. On the other hand, virtual screening searches for commercially available compounds, but imposes challenges when scaling to ultra-large (billion-size and beyond) chemical spaces. Here, we propose a generative design framework that unifies synthesis-constrained molecular design and ultra-large-scale virtual screening through steerable and granular synthesizability control. Generated molecules satisfy arbitrary multi-parameter optimization objectives with predicted synthesis routes satisfying mix-and-match constraints: including or avoiding certain reactions, incorporating specific building blocks, and minimizing synthesis route length. In an end-to-end in-house campaign targeting BRD4, we designed molecules synthesizable with specific selected reactions and building blocks, synthesized all six selected compounds, and identified two micromolar binders. We further demonstrate that reaction control enables efficient navigation of ultra-large make-on-demand chemical spaces to identify property-optimal candidates. By applying our framework to Chemspace's Freedom 4.0 make-on-demand space (142 billion molecules), we generated ~320k molecules (0.00023% of the library) on a single consumer-grade GPU (with only 8 GB GPU memory) and identified a micromolar Wee1 binder amongst 60 synthesized candidates. The single unified framework thus enables generating novel synthesizable molecules and retrieving catalogue-ready candidates, offering a flexible solution to mitigating the synthesizability bottleneck.

q-bio.BM↗

arXiv2020

Correct block-design experiments mitigate temporal correlation bias in EEG classification

It is argued in [1] that [2] was able to classify EEG responses to visual stimuli solely because of the temporal correlation that exists in all EEG data and the use of a block design. We here show that the main claim in [1] is drastically overstated and their other analyses are seriously flawed by wrong methodological choices. To validate our counter-claims, we evaluate the performance of state-of-the-art methods on the dataset in [2] reaching about 50% classification accuracy over 40 classes, lower than in [2], but still significant. We then investigate the influence of EEG temporal correlation on classification accuracy by testing the same models in two additional experimental settings: one that replicates [1]'s rapid-design experiment, and another one that examines the data between blocks while subjects are shown a blank screen. In both cases, classification accuracy is at or near chance, in contrast to what [1] reports, indicating a negligible contribution of temporal correlation to classification accuracy. We, instead, are able to replicate the results in [1] only when intentionally contaminating our data by inducing a temporal correlation. This suggests that what Li et al. [1] demonstrate is that their data are strongly contaminated by temporal correlation and low signal-to-noise ratio. We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2]. Our analyses in this paper refute the claims of the "perils and pitfalls of block-design" in [1]. Finally, we conclude the paper by examining a number of other oversimplistic statements, inconsistencies, misinterpretation of machine learning concepts, speculations and misleading claims in [1].

cs.CV↗

arXiv2020

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features

This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations. We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience work related to visual perception and attention.

cs.CV↗