Physics-Informed Neural Network
PulseAugur coverage of Physics-Informed Neural Network — every cluster mentioning Physics-Informed Neural Network across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New physics-informed learning method for nonlinear system observers
Researchers have developed a novel physics-informed learning approach for creating Kazantzis-Kravaris (KKL) observers for nonlinear systems. This method uses a physics-informed neural network to learn the forward mappin…
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Physics-informed neural network improves battery health prediction
Researchers have developed PiDDM, a novel framework that integrates physics-informed neural networks with degradation kinetics to improve lithium-ion battery state-of-health predictions. By incorporating empirical Arrhe…
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New models compare physics-informed neural networks and tensorial reduced-order models for dam-break simulations
Researchers have developed and compared two parametric data-driven reduced models for the shallow-water dam-break problem. The models, a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order…
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New PINN framework enhances DDFT equation solving with modified activation
Researchers have developed a novel physics-informed neural network (PINN) framework designed to tackle complex nonlocal partial differential equations found in dynamic density functional theory (DDFT). This new approach…
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Physics-informed neural networks model wave propagation in bimaterial systems
Researchers have developed a novel framework utilizing physics-informed neural networks (PINNs) to model elastodynamic wave propagation in bimaterial systems. This approach embeds physical laws directly into the neural …
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New AI Frameworks Enhance PDE Solution Embeddings and Modeling
Researchers have developed a new physics-informed framework that uses multihead Physics-Informed Neural Networks to learn finite-dimensional embeddings of partial differential equation (PDE) solution families. This meth…
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New PINN-GNN framework enhances RF map construction for wireless optimization
Researchers have developed a novel framework for constructing radio frequency (RF) maps using a physics-informed neural network (PINN) combined with a graph neural network (GNN). This approach supports generating new RF…
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New physics-residual network improves hydrogen crossover prediction in PEMWE
Researchers have developed a novel hard-constraint physics-residual network (PR-Net) for predicting hydrogen crossover in polymer electrolyte membrane water electrolysis (PEMWE). This PR-Net integrates fundamental physi…
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New PINN benchmark enhances offshore wind turbine structural monitoring
Researchers have developed a new benchmark called Digi Turbine, designed to improve the reliability of structural health monitoring for offshore wind turbines. This benchmark utilizes Physics Informed Neural Networks (P…
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New Eikonal Caging Method Enhances Robot Manipulation Planning
Researchers have developed a new method called Physics-Informed Eikonal Caging for whole-arm manipulation planning. This approach addresses the challenge of planning complex robot movements that involve extended contact…
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New robotic manipulation planning method uses physics-informed neural networks
Researchers have developed a new method called Physics-Informed Eikonal Caging for whole-arm manipulation planning in robotics. This approach reformulates the concept of 'caging' an object as a minimum-time escape probl…
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PINN framework overcomes noise and dimensionality limits in heat diffusion
Researchers have developed a Physics-Informed Neural Network (PINN) framework to address the limitations of traditional numerical methods like the Finite Difference Method (FDM) when dealing with noisy, high-dimensional…
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New PINN framework integrates literature and network data for microbial modeling
Researchers have developed a novel Physics-Informed Neural Network (PINN) framework that integrates auxiliary knowledge from sources beyond experimental data. This new approach enhances parameter discovery by incorporat…
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New PINN framework solves Fokker-Planck equations for diverse initial conditions
Researchers have developed a new framework using conditional normalizing flows and physics-informed neural networks (PINNs) to solve the Fokker-Planck equation (FPE). This method efficiently approximates the solution op…
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Physics-informed neural networks improve contaminant transport modeling
Researchers have developed a novel two-domain physics-informed neural network (PINN) framework to model contaminant transport through composite liner systems. This framework utilizes a hard-constrained PINN (H-PINN) app…
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PINNs enhance adaptive mesh refinement for PDE solvers
Researchers have developed a novel method that uses Physics-Informed Neural Networks (PINNs) to enhance adaptive mesh refinement (AMR) in finite-difference solvers for partial differential equations (PDEs). This hybrid …
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FEA-PINN accelerates melt pool simulation with comparable accuracy
Researchers have developed a novel framework called FEA-Regulated Physics-Informed Neural Network (FEA-PINN) to accelerate simulations of melt pool dynamics in Laser Powder Bed Fusion (LPBF). This new approach integrate…
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Physics-informed neural networks estimate liquid-liquid separation phase heights
Researchers have developed a novel framework utilizing Physics-Informed Neural Networks (PINNs) to estimate the dense-packed zone height in liquid-liquid separation processes. This approach combines a PINN, pre-trained …
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Physics-informed neural networks simulate pollution spread under thermal inversion
Researchers have developed a robust Physics-Informed Neural Network (PINN) framework to simulate time-dependent pollution propagation, particularly under thermal inversion conditions. This new framework incorporates a r…
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Physics-informed neural network enhances power system security against data attacks
Researchers have developed a new Physics-Informed Neural Network (PINN) designed to enhance the security of power system state estimation against false data injection attacks. This model integrates power-flow consistenc…