Fourier Neural Operators
PulseAugur coverage of Fourier Neural Operators — every cluster mentioning Fourier Neural Operators across labs, papers, and developer communities, ranked by signal.
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LSR-Net architecture learns nonlinear fluid dynamics evolution
Researchers have introduced LSR-Net, a novel neural operator architecture designed to model the forward evolution of nonlinear fluid dynamics. This network effectively learns the evolution operator from initial and futu…
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Fourier Neural Operators Learn Lyapunov Functions for Nonlinear Systems
Researchers have developed a method to approximate Lyapunov functions for nonlinear dynamical systems using Fourier Neural Operators (FNOs). This approach aims to overcome the challenge of finding Lyapunov functions, wh…
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New Transformer-Enhanced Neural Operator Predicts Aerodynamic Performance
Researchers have developed a new method for predicting the aerodynamic performance of turbomachinery cascades, which functions similarly to a CFD simulator. This framework first predicts fundamental parameters of the Na…
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Wavelet-Encoded FNOs Accelerate Metamaterial Design Simulations
Researchers have developed a novel approach using Wavelet-Encoded Fourier Neural Operators (FNOs) to solve complex eigenvalue problems in physics, specifically for metamaterial design. This method effectively predicts m…
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Neural Operators accelerate FitzHugh-Nagumo dynamics modeling
Researchers have developed parameter-conditioned Fourier Neural Operators (FNOs) to create fast, differentiable surrogate models for the FitzHugh-Nagumo (FHN) system. These models can accurately simulate neuronal voltag…
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New research explores adaptable and domain-independent neural operators · 4 sources tracked
Researchers are exploring new methods for neural operators, which are used to approximate physical simulations. One approach, LatentDDM, focuses on pretraining operators on smaller subdomains and then using a lightweigh…
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New MPNO model enhances stability in transient dynamics prediction
Researchers have developed a new neural operator called the Constitutive Markov Physics-Informed Neural Operator (MPNO) designed to improve stability in predicting transient dynamics, particularly for problems with stro…
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AI models benchmarked for real-time tokamak plasma prediction
Researchers have developed an AI surrogate modeling framework to predict tokamak plasma equilibrium in real-time, addressing the computational cost of traditional Grad-Shafranov solvers. The study benchmarks five neural…
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New 'wrong-physics backdoors' found in neural PDE operators
Researchers have identified a new vulnerability in neural partial differential equation (PDE) operators, termed "wrong-physics backdoors." This attack exploits reusable solver archives by subtly altering inputs to trigg…
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New S2RL framework mitigates spectral bias in neural operators for underwater acoustics
Researchers have developed a new framework called Spectral-Spatial Residual Learning (S2RL) to address the spectral bias issue in Fourier Neural Operators (FNOs). This bias causes FNOs to produce over-smoothed predictio…
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New research tackles causal inference challenges in networked systems
Two new research papers explore causal inference in complex systems where effects can spread or interfere. The first paper, "Causal Inference under Interference with Learned Exposure Mappings," investigates how uncertai…
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New pipeline generates ML-ready datasets for 3D fluid dynamics simulations
Researchers have developed ChannelFlow-Tools, an open-source pipeline designed to generate machine-learning-ready datasets for 3D obstructed channel flows. This configuration-driven system integrates procedural obstacle…
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New MoFE framework uses Mixture-of-Experts and FNOs for crypto forecasting
Researchers have introduced MoFE, a novel deep learning framework designed for cryptocurrency forecasting. This framework integrates Fourier Neural Operators (FNOs) within a Mixture-of-Experts (MoE) architecture, aiming…
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Deep learning reconstructs plasma kinetic data from macroscopic measurements
Researchers have developed a deep learning framework to reconstruct detailed kinetic information about low-temperature plasmas from macroscopic measurements. By training neural networks like U-Net, Fourier Neural Operat…
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New L-FNO model enhances prediction of rare events in stochastic systems
Researchers have introduced the Lorentzian Fourier Neural Operator (L-FNO), a novel stochastic neural operator designed to better handle uncertainty and rare events in modern operational systems. Unlike standard neural …
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New ADEx-FNO framework unifies Fourier Neural Operators for complex geometries
Researchers have introduced ADEx-FNO, a novel framework designed to enhance Fourier Neural Operators (FNOs) for applications involving complex and varying geometries. This method embeds physical domains within a fixed a…
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New DeepONet Framework Enhances Accuracy and Efficiency
Researchers have developed new Fixed and Adaptive Topological DeepONets, which improve upon existing Deep Operator Networks by using continuous linear functionals instead of fixed point values for encoding input functio…
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New theory quantifies neural operator approximation in Sobolev spaces
Researchers have developed a new theoretical framework for understanding neural operators' approximation capabilities within Sobolev spaces. This framework establishes an explicit relationship between model complexity a…
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New neural operator frameworks tackle complex partial differential equations · 2 papers
Two new research papers introduce novel neural operator frameworks for solving partial differential equations (PDEs). The first, FB-C2CNet, utilizes fixed bases to encode and decode function coefficients, reducing train…
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New Fourier Neural Operator Extension Tackles Complex PDEs
Researchers have developed an extension to Fourier Neural Operators (FNOs) designed to better model parameterized and coupled partial differential equations (PDEs). The proposed methods incorporate a hypernetwork-based …