Results 41 to 50 of about 18,341 (302)
Parareal with a Physics-Informed Neural Network as Coarse Propagator
, 2023 AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable
Abdul Qadir Ibrahim +2 more
openaire +2 more sources
Probing optimisation in physics-informed neural networks
CoRR, 2023 Accepted at the ICLR 2023 Workshop on Physics for Machine ...
Nayara Fonseca +2 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Pediatric sarcomas are a heterogeneous group of tumors that contribute disproportionately to cancer mortality in children. Although congenital anomalies are among the strongest known risk factors for childhood cancer, the risk of specific sarcoma subtypes among affected individuals has not yet been thoroughly evaluated. Procedure We
Russ Wolters +17 more
wiley +1 more source
PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations [PDF]
Geoscientific Model Development, 2020 Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue.
O. Pannekoucke, R. Fablet
doaj +1 more source
FEBS Letters, EarlyView.Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley +1 more source
Physics-Informed Neural Networks for Power Systems
, 2020 This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field ...
Venzke, Andreas +5 more
core +1 more source
Gradient Statistics-Based Multi-Objective Optimization in Physics-Informed Neural Networks
, 2023 Modeling and simulation of complex non-linear systems are essential in physics, engineering, and signal processing. Neural networks are widely regarded for such tasks due to their ability to learn complex representations from data.
Joachim Denzler, Sai Karthikeya Vemuri
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Physics-Informed Neural Networks for Microprocessor Thermal Management Model
IEEE Access, 2023 The cooling of microprocessors has emerged as a crucial challenge in enhancing performance. Various studies are being conducted to optimize the structural design for microprocessor cooling.
Hwijae Son, Hyunwoo Cho, Hyung Ju Hwang
doaj +1 more source
Preconditioning for Physics-Informed Neural Networks
CoRRPhysics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications.
Songming Liu +6 more
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Phosphoinositides and inositol phosphates as molecular glues
FEBS Letters, EarlyView.Inositol phosphates (IPs) and phosphoinositides (PIPs) regulate diverse eukaryotic processes. Beyond recruiting signaling proteins or acting as structural cofactors, recent studies suggest they mediate protein–protein interactions as natural molecular glues.
Aleshia Seaton‐Terry +9 more
wiley +1 more source