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G. Karniadakis

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Preprint Sep 2026

Site-scale, Multi-resolution WRF Wind Modeling with Observational Nudging for Methane Emission Monitoring in the Permian Basin

Accurate methane source localization and emission-rate estimation at oil and gas facilities require wind fields that resolve site-scale spatial and temporal variability, which cannot be characterized by a single anemometer or coarse operational weather products. We develop a multiscale Weather Research and Forecasting...

Ehsan Kharazmi, Erin Menezes, G. Karniadakis et al. · 0 citations
Open access Dec 2025

Hybrid Iterative Solvers With Geometry‐Aware Neural Preconditioners for Parametric PDEs

The convergence behavior of classical iterative solvers for parametric partial differential equations (PDEs) is often highly sensitive to the domain and specific discretization of PDEs. Previously, we introduced hybrid solvers by combining the classical solvers with neural operators for a specific geometry, but they te...

Youngkyu Lee, Francesc Levrero Florencio, Jay Pathak et al. · 5 citations · ⚡1
#machine learning Preprint Sep 2026

Well-posedness of neural turbulence closures and tangent dissipation

Two complementary ways to promote tangent dissipation are studied: an exact-integral construction enforcing non-negative tangent diffusion while leaving reaction unconstrained, and a penalty on tangent-reaction violations at sampled states that reduces the reported inverse sensitivity relative to the original operator.

Zhen Zhang, G. Karniadakis · 0 citations

GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms

This work proposes a certified regularization for ill-posed in vivo brain-flow reconstruction, developed a spectrally convergent physics-informed architecture, and established machine-checked universal-approximation theorems for two widely used architectures.

Juan Diego Toscano, Zhao-Jie Chai, G. Karniadakis · 10 citations
Preprint Aug 2026

Fixed and Adaptive Topological DeepONets: Functional Measurements on Hausdorff Locally Convex Spaces

The Topological DeepONet framework is built on, replacing point samples by continuous linear functionals drawn from the continuous dual of a Hausdorff locally convex space, whose topology is generated by a point-separating family of seminorms rather than a single norm, and develops fixed and adaptive functional measure...

Khemraj Shukla, G. Karniadakis · 0 citations
Preprint Jul 2026

Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training

Four closures are developed: three model the Reynolds stress on a realizable tensor basis---a local map, a non-local model transporting the turbulent kinetic energy and recovering the out-of-plane normal stress, and a fourth models the Reynolds force directly, free of the realizability constraint.

Zhen Zhang, Theo Kaufer, Louise Ronglan et al. · 0 citations

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