Papers & Surveys

Thermal Management

Proposes a validation-gated multi-agent governance framework enabling thermal-hydraulic surrogate models to adapt online during operating-regime shifts, preventing condition-locking issues in offline-pretrained models.

CFD

Proposes a neural operator-based surrogate model for CFD-level transient thermal-hydraulic simulation of helical coil steam generators in SMRs, enabling real-time digital twin applications.

CFD/Fluid Dynamics

A Transformer-based neural operator approach for 3D wind field prediction over complex mountainous terrain, addressing bottlenecks in traditional CFD methods that require expert-intensive mesh generation and computationally expensive iterative solvers.

Thermal

Therm-FM adapts a pretrained PDE foundation model to steady-state and transient 3D-IC thermal simulation, reducing data-generation costs for cross-design reuse.

ScientificML

Proposes Adaptive Operator Transformation (AOT) to address structural diversity challenges in multi-PDE pre-training by adapting target solution operators rather than increasing model capacity, improving neural operator generalization across equations.

Frontier Paper

Proposes a physics-attention mechanism that projects meshes onto learnable physical-state tokens, improving efficiency and accuracy of PDE solving on large industrial geometries.

Foundational Paper

Presents a branch-trunk architecture realizing a deep learning version of the universal operator approximation theorem - the first systematic architecture for learning nonlinear operators and still a mainstream baseline.

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.