Results 61 to 70 of about 11,198,121 (288)

Physics-Informed Machine Learning for DRAM Error Modeling

2018 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), 2018
As the scale of high performance computing facilities approaches the exascale era, gaining a detailed understanding of hardware failures becomes important. In particular, the extreme memory capacity of modern supercomputers means that data corruption errors which were statistically negligible at smaller scales will become more prevalent.
Elisabeth Baseman, Nathan DeBardeleben, Sean Blanchard, Juston S. Moore, Olena Tkachenko, Kurt B. Ferreira, Taniya Siddiqua, Vilas Sridharan   +7 more
openaire   +3 more sources

Rationally engineered polymeric interfaces for selective and dynamic recognition of vesicular agents

FEBS Open Bio, EarlyView.
This review explores how molecularly imprinted polymers can be engineered to recognize complex nanoscale vesicles, including extracellular vesicles and membrane‐enveloped viruses. By tailoring polymer cavities to vesicle surface chemistry and structure, these synthetic receptors support selective detection, separation, and integration with optical ...
Özgecan Erdem, Eylul Gulsen Yilmaz, Selcan Genc, Nil Kılıç, Fatih Inci   +4 more
wiley   +1 more source

Neural Oscillators for Generalization of Physics-Informed Machine Learning

Proceedings of the AAAI Conference on Artificial Intelligence
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accurate ...
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang 0001, Alfredo Núñez, Rolf P. B. J. Dollevoet   +5 more
openaire   +4 more sources

Early Clinical, Imaging, and Pathological Characteristics of SRPK3/TTN‐Digenic Myopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective SRPK3/TTN‐digenic myopathy was recently established as a skeletal muscle myopathy caused by digenic inheritance. This study characterizes the early clinical presentation of SRPK3/TTN‐digenic myopathy in one previously reported and seven newly identified pediatric patients.
Rotem Orbach, Sandra Donkervoort, Carola Hedberg‐Oldfors, Giovanni Baranello, Dimah Saade, Precilla D'Souza, Ruchee Patel, Eva Michael, A. Reghan Foley, Diana Bharucha‐Goebel, S. Jin Haugland, Meghan McAnally, Omer Abdul Hamid, Katherine Chao, Ellen F. Macnamara, Alan H. Beggs, Anna Sarkozy, Juliane Mueller, Steven A. Moore, Richard S. Finkel, Cynthia J. Tifft, Francesco Muntoni, Anders Oldfors, Carsten G. Bönnemann   +23 more
wiley   +1 more source

Physics-Informed Induction Machine Modelling

, 2023
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
Zhang, Peng, Shen, Qing, Zhou, Yifan
core  

Interpretable Physics-Informed Machine Learning for Pyrolysis

Biomass
Accurate prediction of biomass pyrolysis products remains challenging due to the inherent complexity of thermochemical kinetics and the lack of mechanistic interpretability in modern Machine Learning (ML) models.
Diego Racero Galaraga, Andrea Cressoni De Conti   +1 more
doaj   +1 more source

A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

Scientific Reports, 2021
We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in ...
Shriram Srinivasan, Daniel O’Malley, Maruti K. Mudunuru, Matthew R. Sweeney, Jeffrey D. Hyman, Satish Karra, Luke Frash, J. William Carey, Michael R. Gross, George D. Guthrie, Timothy Carr, Liwei Li, Hari S. Viswanathan   +12 more
doaj   +1 more source

Plasma EV Proteomics Identifies ECM Remodeling and Inflammatory Proteins LUM and C7 as Candidate Biomarkers in FSHD

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Facioscapulohumeral muscular dystrophy (FSHD) is one of the most debilitating and common muscular dystrophies. Despite its severity, no approved therapy exists for FSHD patients. However, several therapeutic candidates are currently under development, and some have recently entered clinical trials, marking the need for reliable ...
Mustafa Bilal Bayazit, Chiranth K. Nagaraj, Jackson S. Newell, Kim Truc Nguyen, Xilal Y. Rima, Jacob Doon‐Ralls, Eduardo Reátegui, Jeffrey M. Statland, Rabi Tawil, Kevin M. Flanigan, Scott Q. Harper, Nizar Y. Saad   +11 more
wiley   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source