TM4FF introduces a physics-constrained operator learning framework combining Transformer attention with Mamba state-space models for accurate flow field prediction and denoising.
NewSee LICENSE fileFlow Field PredictionOperator LearningTransformer
Numerical investigation of how stationary distributions concentrate onto large scales in the fast-advection limit of stochastically forced 2D incompressible Navier-Stokes equations at N=128 resolution.
A reduced-order physical model describes re-ignition dynamics between consecutive discharges at the same position in dielectric barrier discharge arrangements, enabling time prediction without machine learning.
A topology-aware GCN model for fault classification in distribution networks with inverter-induced harmonic distortion using IEEE 34-bus system topology.
Proposes hybrid neural density estimation combining neural ratio estimation with flow-based models to address density normalization and efficient sampling in high-dimensional unbinned simulation-based inference.
NewSee LICENSE fileSimulation-Based InferenceNeural Density EstimationFlow Models
Proposes a physics-informed ML framework combined with multi-objective optimization for inverse design of Co-based superalloy compositions to enhance γ' solvus temperature while considering alloy density.
A periodic physics-informed neural network (P-PINN) framework is proposed for solving parametric periodic flows and analyzing finite-amplitude aeroelastic responses in a purely physics-driven manner.
Proposes Interface Autoencoder (IAE) for learning neural operators on geometry-dependent PDEs, representing interface-function states in a fixed domain via finite projection codes.
This paper formulates optimal biasing as an ergodic optimal control problem, recast as a linear eigenvalue problem for the infinitesimal generator of unbiased dynamics to learn bias potentials that accelerate transitions between metastable states in molecular dynamics.
Proposes active self-consistent field theory (ASCFT) for studying steady-state behavior of active Ornstein-Uhlenbeck polymers, extending classical polymer field theory to non-equilibrium systems.
NewSee LICENSE fileActive PolymersSelf-Consistent Field TheoryNon-Equilibrium Statistical Mechanics
This paper reveals the multi-conformal symplectic structure preserved by Hamiltonian PDEs regularized via Brinkman penalization, proving an exact local conservation law under a compatibility condition linking the symplectic matrix and penalization projection.
Investigates how load information quality affects power grid control using a DC power flow model and Grid2Op-inspired simulator, exploring data assimilation combined with reinforcement learning strategies.
Proposes a harmonic-regression combined ARMA model for fault detection in rotating machinery, addressing limitations of purely rational spectrum assumptions under varying operating conditions.
NewSee LICENSE filerotating machineryfault detectionARMA model
This paper develops an analytical and numerical framework for temporal scattering in electromagnetic metamaterials with shape-varying cylindrical inclusions, enabling dynamic control through abrupt geometric transformations of meta-atoms.
Proposes using ordinary multilayer perceptrons with finite-group averaging to achieve exact equivariant surrogate models for correlated electron systems, enabling efficient force and transition energy evaluations.
Uses patch-based autoencoder and neural ODE to build a distributed reduced-order model for studying relative periodic orbits in 2D Kolmogorov turbulence.
NewSee LICENSE fileKolmogorov turbulenceperiodic orbitreduced-order model
A self-driving laboratory strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions to accelerate optimization of multicomponent NASICON fast ionic conductors while maintaining phase purity.
This survey covers brain-to-language decoding techniques, including neural signal acquisition, representation learning, and text generation, applicable to brain-computer interfaces and speech restoration.
NewSee LICENSE fileBrain-computer interfaceNeural decodingLanguage model
A physics-informed computational screening framework that decomposes effective second-harmonic response to optimize nonlinear optical properties of 2D semiconductor/organic molecule interfaces.
Investigates ferroelectric switching modeling by comparing four cost-effective DFT methods against high-accuracy ab initio approaches, aiming to establish reliable benchmarks for active learning workflows.
