Papers & Surveys

Electromagnetics

K-TRAIL is a simulator-guided generative framework combining diffusion-based layout generation with derivative-free ensemble Kalman guidance for automated EM/RF circuit inverse design.

Electromagnetics

A GPU-accelerated implicit solver for electromagnetic field computations in full 3D tokamak simulations, featuring co-designed hierarchical domain decomposition and fast exact local solver for efficient large-scale parallel GPU computing.

ScientificML

This paper presents a comparative analysis of two neural network models for Initial Orbit Determination from Very Short Arcs, addressing challenges in Near-Earth Object surveillance where classical methods fail due to insufficient observational arcs.

Structural

Investigates the effectiveness of Physics-Informed Neural Networks in simulating transverse vibration of one-dimensional linear stiff strings by incorporating PDE constraints and boundary conditions into the loss function.

Multibody

ForceTwin learns joint physical properties including inertia, friction, and spring mechanisms from human interaction data to build physics-informed digital twins that predict robotic manipulation actions.

CFD

Proposes periodic Fourier Neural Mapping (p-FNM), a neural-operator framework for rapid prediction of unsteady pressure distributions on turbine rotor blades, targeting aeroelastic analysis in turbomachinery.

Multibody

LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, structuring the latent space so that cost-minimizing solutions are placed at the origin, enabling efficient sampling of approximately optimal inverse kinematics solutions.

Multibody/Robotics

PIVOT combines physically-informed reasoning with vision-language models for off-road traversability assessment, enhancing navigation safety in unstructured terrain for field robots.

ScientificML

Proposes a graph hypernetwork approach using an operator graph that explicitly represents PDE fields, derivatives, terms, and residuals as nodes, enabling amortized neural solver meta-training across related partial differential equations.

Biomechanics / Computational Medicine

This paper introduces a Bayesian surrogate-modeling framework for inferring regional Holzapfel-Ogden material parameters in a left ventricle partitioned into five physiological zones, using cardiac MRI data to overcome the limitation of treating the myocardium as mechanically homogeneous.

ScientificML

A unified framework combining physics-informed neural networks with finite element methods via functional Gauss-Newton problems and Petrov-Galerkin discretization.

Structural

Proposes a ROM-based preconditioner to accelerate topology optimization, reducing computational cost of high-fidelity finite element analysis in structural conceptual design.

Geophysics

A continental-scale electrical resistivity model of Australia's shallow crust (up to ~650 m depth) derived from probabilistic deep learning inversion of 26.6+ million AEM soundings, with quantified uncertainty for geology, groundwater, and critical mineral exploration.

ScientificML

Proposes a Bayesian decision-theoretic framework for multiobjective optimization under uncertainties, maximizing the expected hypervolume with respect to input points using gradient-based stochastic optimization.

Quantum

Using physics-informed neural networks to solve electron spin dynamics in few-cycle laser pulses, deriving analytical expressions for the polarization angle of electrons initially at rest in CEP-stabilized linearly polarized plane-wave pulses.

ScientificML

PosteriorBench introduces a benchmark for evaluating how well generative inverse solvers capture true posterior distributions in scientific inverse problems, addressing limitations of pointwise accuracy metrics.

ScientificML

Proposes a physics-informed inverse modeling framework to infer governing parameters of the Moose-Wolf ecosystem in Isle Royale National Park from limited and noisy observational data, providing a data-driven approach for ecological dynamics modeling.

ScientificML

This paper proposes PIRNN to enhance interpretability and accuracy of time series forecasting for physical processes by incorporating physical knowledge from historical data during training.

ScientificML

Proposes a ladder of information limits to quantify predictability boundaries for reduced-order models under limited information, covering memoryless and memory-augmented trajectory forecasts, event prediction, stationary statistics, and generative laws.

CFD

Presents physics-informed hemodynamic modeling for coronary intervention, enabling data-free prediction and sparse-data assimilation to overcome limitations of traditional numerical analysis and purely data-driven approaches.