Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

• URL: http://arxiv.org/abs/2405.05908v4
• Date: Tue, 05 Nov 2024 18:17:06 GMT
• Title: Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas
• Authors: Azarakhsh Jalalvand, SangKyeun Kim, Jaemin Seo, Qiming Hu, Max Curie, Peter Steiner, Andrew Oakleigh Nelson, Yong-Su Na, Egemen Kolemen,
• Abstract summary: A non-linear system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic. We introduce a machine learning methodology to address this issue. We use other diagnostics to produce super-resolution data, capturing detailed structural evolution and responses to perturbations previously unobservable.
• Score: 4.606409469837236
• License:
• Abstract: A non-linear system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view, leading to information loss. Combining multiple diagnostics may also result in incomplete projections of the system's physics. By identifying hidden inter-correlations between diagnostics, we can leverage mutual support to fill in these gaps, but uncovering such correlations analytically is too complex. We introduce a machine learning methodology to address this issue. Unlike traditional methods, our multimodal approach does not rely on the target diagnostic's direct measurements to generate its super-resolution version. Instead, it uses other diagnostics to produce super-resolution data, capturing detailed structural evolution and responses to perturbations previously unobservable. This not only enhances the resolution of a diagnostic for deeper insights but also reconstructs the target diagnostic, providing a valuable tool to mitigate diagnostic failure. This methodology addresses a key challenge in fusion plasmas: the Edge Localized Mode (ELM), a plasma instability that can cause significant erosion of plasma-facing materials. A method to stabilize ELM is using resonant magnetic perturbation (RMP) to trigger magnetic islands. However, limited spatial and temporal resolution restricts analysis of these islands due to their small size, rapid dynamics, and complex plasma interactions. With super-resolution diagnostics, we can experimentally verify theoretical models of magnetic islands for the first time, providing insights into their role in ELM stabilization. This advancement supports the development of effective ELM suppression strategies for future fusion reactors like ITER and has broader applications, potentially revolutionizing diagnostics in fields such as astronomy, astrophysics, and medical imaging.

Related papers

• MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models [49.765466293296186]
  Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. Med-LVLMs often suffer from factual hallucination, which can lead to incorrect diagnoses. We propose a versatile multimodal RAG system, MMed-RAG, designed to enhance the factuality of Med-LVLMs.
  arXiv  Detail & Related papers  (2024-10-16T23:03:27Z)
• CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical Imagery [0.0682074616451595]
  CAF-YOLO is a nimble yet robust method for medical object detection that leverages the strengths of convolutional neural networks (CNNs) and transformers. ACFM module enhances the modeling of both global and local features, enabling the capture of long-term feature dependencies. MSNN improves multi-scale information aggregation by extracting features across diverse scales.
  arXiv  Detail & Related papers  (2024-08-04T01:44:44Z)
• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• Integrating LLMs for Explainable Fault Diagnosis in Complex Systems [0.0]
  This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solution that not only identifies faults but also provides clear, understandable explanations of their causes and implications.
  arXiv  Detail & Related papers  (2024-02-08T22:11:21Z)
• A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders [11.622160966334745]
  We utilize both MRIs and fMRI data and propose a multimodal diagnosis model for bipolar disorder. Our proposed method outperforms others in balanced accuracy from 0.657 to 0.732 on the OpenfMRI dataset.
  arXiv  Detail & Related papers  (2024-01-15T10:11:19Z)
• Diffusion MRI with Machine Learning [1.9798034349981164]
  Diffusion-weighted magnetic resonance imaging (dMRI) offers unique capabilities. Machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis.
  arXiv  Detail & Related papers  (2024-01-01T13:03:35Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Interpretable multimodal fusion networks reveal mechanisms of brain cognition [26.954460880062506]
  We develop an interpretable multimodal fusion model, gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCL's performed well for both classification and mechanism analysis.
  arXiv  Detail & Related papers  (2020-06-16T18:52:50Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.