Papers & Surveys

ScientificML

Proves the original BGN method for mean curvature flow converges with order h² using backward error analysis from ODE numerical analysis.

CFD

Proposes a Runge-Kutta aligned oscillation-eliminating DG framework that fixes the disruption of error structures caused by nonlinear stabilization, enabling superconvergence analysis for fully discrete shock-capturing DG schemes.

ScientificML

Presents AKAPINN, a mesh-free KAN-PINN method for nonlinear strain-limiting PDEs with exact hard boundary ansatz and pseudo-Huber loss, evaluated across three material-parameter pairs.

Electromagnetics

Proposes a coupled Maxwell-cable framework integrating FDTD solutions with Hodgkin-Huxley and Fitzhugh-Nagumo dynamics to simulate action potential propagation in branched neuronal geometries.

Structural

This paper rigorously establishes inf-sup stability of the Scott-Vogelius finite element on three-dimensional Freudenthal meshes for polynomial degree k ≥ 4, completing the theoretical framework for this classical divergence-free mixed element used in Stokes flow computations.

ScientificML

Investigates how uncertainty quantification approaches perform when adapted to geometry-conditioned neural surrogates for CAE, comparing closed-form and sampling-based methods across varying geometries and engineering quantities of interest.

Quantum/Chemical

MARC-TS introduces a two-stage framework that learns endpoint-conditioned continuous reaction paths, queries at arbitrary resolution, and refines transition-state candidates via local path context, with T1x-IRC-8K dataset of 8,209 reactions and 1.09M path-resolved geometries.

ScientificML

Proposes a tensor product multilevel method (TPML) combining Smolyak's sparse grids with kernel-based interpolation, coupling kernel scale to fill distance for uniform condition number bounds in high-dimensional function approximation.

ScientificML

This paper introduces ICG-MTO, a framework that leverages numerical foundational models to improve inter-task coupling estimation in few-shot multi-task optimization scenarios, addressing negative transfer issues.

Materials

Molecular dynamics simulations investigate how local chemical clustering affects structural and functional properties of Ba(Zr_xTi_{1-x})O_3 in ferroelectric and relaxor regimes, revealing mechanisms of chemical inhomogeneity on material performance.

Multibody/Robotics

Proposes adaptive interaction strategies for companion robots using deep reinforcement learning, overcoming traditional fixed-position tracking limitations to enable flexible robot accompaniment in dynamic workspaces.

Materials

Presents a physics-informed ML framework for inverse design of Co-based superalloys, using atomic size mismatch and other descriptors with multi-objective optimization to enhance gamma-prime solvus temperature while constraining density.

CFD/Fluid

This study evaluates physics-informed neural networks for atmospheric potential-temperature forecasting, constrained by pressure-coordinate thermodynamic advection-source equations to address short-horizon predictions under sparse thermal observation conditions.

Electromagnetics

Demonstrates a large-scale metasurface-based reflection cavity five-layer diffractive neural network achieving angle-multiplexed multi-task processing, addressing scalability limitations in all-optical computing.

Multibody

This paper proposes a separated-section constitutive law for Cosserat rod modeling of trimmed helicoid soft robot arms, evaluating each helix domain's mechanical response in its local frame and mapping it back to the global coordinate system for improved modeling accuracy.

ScientificML

This paper studies the convergence properties of the cyclic steepest descent (CSD) method for minimizing strictly convex quadratic functions in arbitrary dimensions, proving doubly exponential convergence rates and extending prior theoretical results limited to two-dimensional cases.

High-Energy Physics

A physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in multi-wire parallel plate avalanche counters, enhancing adaptability to changing operating conditions and detector aging.

Electromagnetics

Presents a fully implicit PIC method extended to electromagnetic cases with exact conservation of global energy and local charge, enabling plasma equilibrium simulations at high (up to extreme) β in open magnetic traps.

Original Research Paper

ICML 2026 paper proposing APT, a geometry-/mesh-/physics-agnostic neural operator that fuses graph-based local feature extraction with global attention for long-range physical effects; achieves 4× saturation error reduction on 2D faulted CO2 storage vs prior SOTA, 19,873× speedup over GEOS and 6,800× over ECLIPSE.

Original Research Paper

ICLR 2026 paper proposing ATOM, a quasi-equivariant pretrained transformer neural operator that requires no explicit molecular graph and uses temporal attention for parallel decoding of multiple future states; curates TG80 (80 compounds, 2.5M fs trajectories) and demonstrates zero-shot generalization on MD17/RMD17/MD22.