physics-informed neural networks
PulseAugur coverage of physics-informed neural networks — every cluster mentioning physics-informed neural networks across labs, papers, and developer communities, ranked by signal.
- instance of Physics-Informed Neural Network 95%
- used by partial differential equations 90%
- instance of alphaXiv 90%
- developed HSPINN 90%
- used by partial differential equation 90%
- used by Ordinary Differential Equations 80%
- instance of partial differential equations 70%
- instance of ScienceCast 70%
- competes with finite element method 70%
- developed by Zhangyong Liang 70%
- used by convection 70%
- instance of partial differential equation 70%
18 day(s) with sentiment data
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New Weak-Entropy PINN framework tackles discontinuous solutions in hyperbolic conservation laws
Researchers have developed a novel Weak-Entropy PINN (WEPINN) framework to address the challenge of solving hyperbolic conservation laws with discontinuous solutions using neural networks. This new method enforces gover…
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New research explores finite-difference methods for PINNs
A new paper explores the use of finite-difference (FD) methods for computing derivatives in Physics-Informed Neural Networks (PINNs), presenting it as an alternative to automatic differentiation (AD). The research demon…
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Physics PhD candidate seeks transition to ML Engineering role
A Ph.D. candidate in electrical engineering with a focus on quantum optics and photonics is seeking advice on transitioning into a Machine Learning Engineering role. The individual highlights extensive software developm…
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ProPINN architecture tackles propagation failures in physics-informed neural networks
Researchers have introduced ProPINN, a novel architecture designed to address propagation failures in physics-informed neural networks (PINNs). These failures occur when supervision signals from initial or boundary cond…
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Eikonal Regularisation in PINNs for 3D Fluid Dynamics Explored
A new research paper explores the effectiveness of Eikonal Regularisation in physics-informed neural networks (PINNs) for simulating three-dimensional fluid dynamics. The study, authored by Muhammad Akbar Khan, investig…
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New FBPINNs method improves fluid flow simulation in perforated domains
Researchers have developed a new method called finite basis physics-informed neural networks (FBPINNs) to more accurately simulate viscous fluid flow in highly perforated domains. Traditional physics-informed neural net…
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New Hierarchical Representation Boosts Physics-Informed Neural Networks
Researchers have introduced a novel Hierarchical Rank-Evolving (HRE) representation designed to enhance physics-informed neural networks (PINNs). This new method addresses limitations in existing tensor-based PINNs by a…
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New Metaplectic Neural Networks Show Promise for Schrödinger Equation Approximation
Researchers have developed a new type of shallow neural network utilizing a dictionary based on metaplectic operators. This approach extends the concept of Barron spaces by incorporating a metaplectic transform, a sympl…
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New PINN training methods tackle high-frequency and parameterized PDEs · 4 sources tracked
Researchers have developed new methods to improve the training of physics-informed neural networks (PINNs), addressing challenges like spectral bias and representation-coefficient coupling. One approach, IFeF-PINN, uses…
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New SCORE method enhances physics-informed neural network training
Researchers have developed SCORE, a novel self-concordance-inspired quasi-Newton method designed to improve the training of physics-informed neural networks (PINNs). This method addresses challenges with indefinite and …
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New GeoID-PINN model enhances regional epidemic forecasting
Researchers have developed GeoID-PINN, a novel physics-informed neural network designed for regional epidemic forecasting. This model specifically addresses the challenge of disentangling local transmission, reporting, …
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New research explores cardiovascular digital twins and calibration methods · 2 sources tracked
Two new arXiv papers explore the development and calibration of digital twins for cardiovascular health. The first paper reviews various modeling approaches, from physics-based to data-driven, highlighting the integrati…
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Quantum-Classical Framework Boosts PINN Accuracy for Complex Equations
Researchers have developed a hybrid quantum-classical framework to improve the accuracy and efficiency of Quantum Physics-Informed Neural Networks (QPINNs) for solving complex differential equations. This new approach i…
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New framework bridges AI and power engineering education · 2 sources tracked
A new framework, Engineering-Grounded AI (EGAI), has been developed to integrate artificial intelligence into power and energy systems education. This framework, presented as a collection of open, executable Jupyter not…
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New PINN Framework Accurately Models Plasma Drift Waves
Researchers have developed a novel physics-informed neural network (PINN) framework to accurately identify complex eigenfrequencies and reconstruct mode structures for ion-temperature-gradient (ITG) drift waves. This ne…
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New Feature Interaction Models Enhance Physics-Informed Neural Networks
Researchers have developed new methods to enhance the expressiveness of physics-informed neural networks (PINNs) and neural operators. By incorporating feature interaction modules inspired by factorization machines, the…
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New framework uses physics-informed GNNs for faster, more robust power flow solutions
Researchers have developed PINCO, a novel unsupervised learning framework that combines Graph Neural Networks with physics-informed neural networks to solve AC optimal power flow (AC-OPF) problems. This approach enhance…
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New framework uses Equivariant Neural Fields for scalable travel-time prediction
Researchers have introduced Equivariant Neural Eikonal Solvers, a new framework that combines Equivariant Neural Fields with Neural Eikonal Solvers. This approach uses a shared neural network backbone conditioned on sig…
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New PIKS method offers universal physics-informed kernel learning
Researchers have introduced Physics-Informed Kernel methodS (PIKS), a novel approach to physics-informed machine learning that aims to overcome the limitations of existing methods. Unlike physics-informed neural network…
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AI agents discover physics mappings and new algorithms for neural networks · 2 sources tracked
Two new research papers explore the use of AI agents in scientific discovery, specifically within physics and computational mathematics. The first paper introduces StatMechBench-v0, a benchmark designed to test AI agent…