Papers & Surveys

Geophysics

FREESIA addresses multimodal posterior estimation under high-dimensional sparse observations by using covariance-aware posterior transport, improving inference accuracy for nonlinear or non-injective observation operators in dynamical systems.

CFD

Proposes an integrated CFD-ML framework for optimizing pod geometry in supersonic evacuated tube transport, leveraging Latin Hypercube Sampling and Bezier curve parameterization for aerodynamic design.

Quantum/Chemical

This paper presents a privacy-preserving hybrid quantum-classical computing framework that integrates parameterized quantum circuits with classical components, targeting quantum computing proxy applications such as chemistry simulation.

Materials

Proposes high-order variable-scaled energetic variational neural networks (VS-EVNN) for phase-field gradient flows, using interface-scale normalization of spatial inputs to improve discretization accuracy.

CFD

This paper presents an unconditionally stable second-order numerical method for fluid-poroelastic structure interaction, where free fluid governed by incompressible Stokes equations couples with poroelastic medium via the Biot system, reformulated using Robin-type interface conditions.

ScientificML

A deep-learning surrogate model based on residual CNN predicts Earth-induced transition probabilities for solar neutrinos across energy and zenith angle parameter space.

Quantum/Chemistry

Proposes finite-state automata encoding recursive lattice geometry for exact MPO construction of tight-binding Hamiltonians across fractals, quasicrystals, and hyperbolic lattices.

Quantum / Chemistry

A probabilistic physics-informed framework using WaveNet and ParamNet to solve the inverse problem of recovering Woods-Saxon nuclear potential parameters from sparse spectral data.

Structural

A modal integrator framework (MIISO) is proposed to stabilize non-physical outlier modes at the top of the discrete spectrum in high-order isogeometric discretizations, improving numerical solutions in wave propagation and structural dynamics.

Electromagnetics

DNNsolver proposes a deep learning approach for accurate and efficient modeling of polarization-sensitive diffractive neural networks, addressing limitations of thin element approximation in capturing intra-layer electromagnetic coupling.

Materials

Investigates the relationship between residual scale and learnability in Δ-learning, demonstrating through molecular graph neural network evaluation on total energy targets that smaller residual targets do not necessarily yield better learning outcomes.

Multibody

GINIO introduces a geometric SO(3)-equivariant interface ensuring consistent neural inertial odometry measurements under arbitrary IMU mounting orientations in filtering pipelines.

ScientificML

DissipNet enforces dissipativity constraints to model dissipative dynamical systems, balancing prediction accuracy with physical property preservation for control theory and complex dynamics analysis.

Multibody

HOTICE introduces Humanoid-Object Decoupled Potential Fields for joint collision encoding, enabling whole-body coordination and collision avoidance during object transportation in cluttered environments.

Multibody

JAMB proposes jointly denoising bimanual actions and motion states, explicitly modeling geometric interactions between arms in shared 3D scenes to enable coordinated manipulation.

Structural

KATOsuper presents an objective-agnostic framework that couples neural-reparameterized topology optimization with sensitivity-consistent Fourier neural operators, addressing gradient inconsistency between predicted objectives and sensitivities for stable structural optimization.

ScientificML

A high-performance PDE neural solver based on Manifold Function Encoder, featuring resolution-independent memory footprint and arbitrary PDE input support for large-scale physical simulations.

Electromagnetics

Proposes a geometric constellation shaping (GCS) framework to enhance sensing privacy in OFDM-ISAC systems, addressing the privacy risk where passive eavesdroppers exploit ISAC data signals for unauthorized target sensing, revealing receiver-specific ranging performance dependent on constellation design.

Geophysics

PISCES is a convolutional autoencoder trained without catalog labels on OMNI solar wind measurements under physics constraints, designed for detecting solar wind transients and providing space weather early warning at the Sun-Earth L1 point.

Multibody

A Transformer-based warm-starting approach for non-convex trajectory optimization in cislunar rendezvous, proximity operations, and docking scenarios.