Papers & Surveys

CFD

A comparative study benchmarking autoencoders against parsimonious diffusion maps (PDMs) grounded in classical numerical analysis for reduced-order modeling of Navier-Stokes turbulent flows.

ScientificML

Proposes the Fractional Fourier Neural Operator (FrFNO) that embeds the analytic Mittag-Leffler propagator to address the computational challenge of memory-dependent time derivatives and nonlocal fractional Laplacians in fractional PDEs, enabling efficient surrogate modeling.

ScientificML

Proposes a triply-scalable equivariant Gaussian process method that encodes symmetries while maintaining probabilistic predictions and uncertainty quantification for large-scale scientific computing.

Materials

Presents the first complete machine learning method for accelerating plane-wave DFT in materials under the PAW formalism, formalizing seven criteria that a Complete Neural Electronic Initializer must satisfy and addressing two missing structure-dependent components: augmentation occupancies and spin initialization.

ScientificML

A joint training approach that addresses neural operator out-of-distribution degradation and PINN multi-solution convergence errors by leveraging imperfect neural operators to guide PINN optimization.

ScientificML

Proposes a kernel estimator for nonparametric tangent vector field regression on Riemannian manifolds without boundary, using parallel transport and volume correction, with theoretical derivation of uniform second-order bias and stochastic convergence rates.

ScientificML

Introduces backward logarithmic orthogonal functions constructed via terminal-endpoint logarithmic mapping of generalized Laguerre polynomials, designed for backward spectral approximation of problems with weak endpoint singularities.

ScientificML

Proposes Transformer-based learning of structured representations for symbolic ODE expressions, preserving commutative properties to enable mathematical reasoning in symbolic form, distinct from traditional methods relying on numerical fitting.

Multibody/Robotics

This paper addresses kinematics estimation and closed-loop control for tensegrity-based continuum robots by parameterizing complex continuous bending configurations using strut inclination angles, overcoming limitations of elastic backbone models.

ScientificML

Proposes a low-rank alternating direction implicit method for solving large-scale discrete-time non-symmetric algebraic Riccati equations arising in game-theoretic Nash equilibrium computations, significantly reducing storage and computational complexity.

ScientificML

Exploring vision-language models for jet analysis in high-energy physics, converting JetClass jets into physics-informed four-panel images for multi-task jet tagging classification.

ScientificML

NeuIDO proposes a neural intrinsic dynamics operator that internalizes physical constraints into world models for predicting 3D object interaction trajectories, bridging the gap between physics-informed 4D generation and true world models.

ScientificML

Proposes a continuous delayed-memory stochastic gradient descent method combined with neural network-parameterized stochastic differential equations to model quasar brightness variations from astronomical survey time series.

Quantum/Chemical

Proposes iCRNNs to discover chemical reaction network structures and kinetics from time-series concentration data without prior mechanistic assumptions, balancing interpretability and computational efficiency.

ScientificML

Proposes a novel framework using set-based value function characterization and neural network approximation to estimate stabilization domains and stabilizing controllers for input-constrained discrete-time nonlinear systems.

CFD

This paper proposes a periodic activation function architecture using complex exponential to generate paired sine and cosine outputs, demonstrating improved performance over sine-activated MLPs for reconstructing convective flows in physics-informed neural networks.

CFD

Proposes p-FNM, a neural-operator framework using periodic Fourier mapping to predict unsteady pressure distributions and aeroelastic loads on turbine rotor blades, reducing high-fidelity CFD computational costs.

ScientificML

GravINNs embeds post-Newtonian equations of motion directly into neural network training to learn binary orbital dynamics, including single-orbit surrogates and extensions across a four-dimensional parameter space.

Multibody

Proposes a PINN-based approach for the weakly coupled two-compartment neuron model to infer unmeasurable somatic membrane potential dynamics from dendritic recordings.

Electromagnetics

Develops a droop-aware extension of GridFM foundation model for power systems, embedding droop gains and deadband parameters as node features for control-aware AC power flow analysis.