Papers & Surveys

Foundational Paper

Introduces neural operators with integral kernels parameterized in Fourier space, enabling resolution-invariant PDE solving - a foundational and highly cited work in physics AI.

Must-Read Benchmark Paper

Proposes a unified benchmark across 16 physical domains, arguing for cross-domain pretraining gains - a must-read for physics foundation models.

ScientificML

Proposes a geometry-aware incremental neural operator to address error accumulation in long-horizon autoregressive PDE prediction, mitigating spectral inconsistency and unstable channel couplings.

ScientificML

HIPNO leverages symmetry-aware physics-informed neural operators to infer hemodynamic states from non-invasive signals, addressing scale symmetry challenges in physics-informed hemodynamic inference and expanding access to advanced monitoring.

Electromagnetics

DNNsolver introduces a deep learning approach for modeling polarization-sensitive diffractive neural networks, addressing the challenge of accurate and efficient electromagnetic coupling simulation within cascaded diffractive layers.

Multibody

Proposes bilinear Koopman model-based diffusion (BK-MBD) to reduce computational cost in real-time robot control by decoupling state lifting from candidate propagation.

CFD

Introduces mollified-sharp decomposition for probabilistic regularization of moving shocks in parametric reduced-order models, enhancing shock-capturing capabilities in CFD applications.

Electromagnetics

Presents a general method for producing stochastic synthetic axion waveforms by shaping Gaussian white noise in the frequency domain, targeting microwave cavity haloscope calibration and analysis pipeline validation for dark matter axion searches.

ScientificML

Investigates three variational objectives for VAE-based amortized Bayesian inference in inverse problems, revealing how posterior regularization impacts weakly identified parameter directions.

Electromagnetics

Gnomon introduces a physics-informed system that reconstructs LEO satellite serving geometry and signal attenuation to forecast user-perceived downlink/uplink throughput and RTT under varying trace availability.

Acoustics

A browser-based acoustic simulator and inverse design tool for wind instrument bores without tone holes, using Transfer Matrix Method for forward modeling and adjoint-based Tikhonov regularization for inverse design optimization.

ScientificML

Proposes combining Artificial Representative Trees with leaf-wise Mondrian Conformal Predictive Systems to enable a single decision tree to provide continuous predictions, prediction intervals, and probabilistic outputs as an interpretable ML approach.

ScientificML

The Fractional Fourier Neural Operator (FrFNO) integrates the analytic Mittag-Leffler propagator into a Fourier-based neural operator to provide a resolution-robust surrogate for space-time fractional PDEs with memory-dependent derivatives.

Electromagnetics

A novel computationally efficient surrogate model for linear mode coupling in graded-index multimode fibers, tunable via a single measurable parameter, benchmarked numerically and experimentally on a 90-polarization-mode fiber.

CFD

Proposes WaveOp-LiteFM, a lightweight neural-operator flow matching approach that addresses the trade-off between computational cost and accuracy in satellite-to-radar precipitation retrieval for atmospheric remote sensing applications.

ScientificML

Proposes a nonlinear reduced-order model with convolutional operator compression to overcome the Kolmogorov N-width limitation of linear projection-based ROMs, achieving accurate approximations with fewer modes for parametrized PDEs.

Plasma / Fusion

Presents PQLS, a quasilinear gyrokinetic transport solver using an eigenvalue solver in general magnetic geometry with Bayesian saturation-rule closure to improve turbulent transport prediction in tokamak plasmas.

ScientificML

This paper proposes an improved periodic activation using complex exponential functions generating paired sine-cosine outputs for Physics-Informed Neural Networks, demonstrating superior performance over sine-activated MLPs in reconstructing temperature fields from sparse velocity data in convective flows.

Multibody

This paper solves the four-dimensional HJB equation for two diffusively coupled Stuart-Landau-like oscillators to obtain full-state optimal feedback control, revealing a sharp transition in control strategy from phase synchronization to amplitude collapse at a critical coupling threshold.

ScientificML

Proposes block-triangular joint drifting to overcome the challenge of estimating conditional distributions in stochastic transition modeling, enabling one-step generative surrogate models for trajectory data prediction.