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Partial Differential Equations (PDEs) are notoriously difficult to solve. In general, closed-form solutions are not available and numerical approximation schemes are computationally expensive. In this paper, we propose to approach the…

Machine Learning · Computer Science 2022-03-23 Nils Wandel , Michael Weinmann , Michael Neidlin , Reinhard Klein

We establish convergence rates for a fully discrete, multi-level, linear collocation method solving parametric elliptic PDEs on bounded polygonal domains with log-normal inputs. The method uses a finite set of function evaluations in the…

Numerical Analysis · Mathematics 2026-03-30 Dinh Dũng

We employ density functional theory to calculate the self consistent electronic structure, free energy and linear source-drain conductance of a lateral semiconductor quantum dot patterned via surface gates on the 2DEG formed at the…

Condensed Matter · Physics 2009-10-28 M. Stopa

We derive general bounds on operator dimensions, central charges, and OPE coefficients in 4D conformal and N=1 superconformal field theories. In any CFT containing a scalar primary phi of dimension d we show that crossing symmetry of <phi…

High Energy Physics - Theory · Physics 2011-05-09 David Poland , David Simmons-Duffin

Logarithmic Conformal Field Theories (LCFT) play a key role, for instance, in the description of critical geometrical problems (percolation, self avoiding walks, etc.), or of critical points in several classes of disordered systems…

High Energy Physics - Theory · Physics 2013-11-22 A. M. Gainutdinov , J. L. Jacobsen , N. Read , H. Saleur , R. Vasseur

In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods…

Machine Learning · Computer Science 2024-04-24 Adrian Celaya , Keegan Kirk , David Fuentes , Beatrice Riviere

We present new numerical results on the space of local, unitary, parity-preserving conformal field theories (CFTs) in three dimensions from the stress tensor bootstrap. In bounds maximizing certain OPE coefficients, we find a plethora of…

High Energy Physics - Theory · Physics 2026-02-17 Rajeev S. Erramilli , Matthew S. Mitchell

This is a set of notes which reviews and addresses issues in the SL(2,R) conformal field theory, while working primarily in a basis of vertex operators of definite weight under the affine algebra. Following a review of the H3 coset model…

High Energy Physics - Theory · Physics 2015-11-24 Will McElgin

This paper presents a learnable solver tailored to iteratively solve sparse linear systems from discretized partial differential equations (PDEs). Unlike traditional approaches relying on specialized expertise, our solver streamlines the…

Numerical Analysis · Mathematics 2024-05-10 Yan Xie , Minrui Lv , Chensong Zhang

Stochastic partial differential equations (SPDEs) describe the evolution of random processes over space and time, but their solutions are often analytically intractable and computationally expensive to estimate. In this paper, we propose…

Machine Learning · Computer Science 2025-08-12 Ísak Pétursson , María Óskarsdóttir

In this paper, we use crossing symmetry and unitarity constraints to put a lower bound on the central charge of conformal field theories in large space-time dimensions $D$. Specifically, we work with the four-point function of identical…

High Energy Physics - Theory · Physics 2023-06-07 Abhijit Gadde , Mrunmay Jagadale , Shraiyance Jain , Trakshu Sharma

The N = 2, 4 superconformal symmetry constraints in d = 4 for four point functions of chiral primary 1/2-BPS operators are derived. The operators are described by symmetric traceless tensors of the internal R-symmetry group. A substantial…

High Energy Physics - Theory · Physics 2017-06-06 Michael Nirschl

We use the superconformal bootstrap to derive exact relations between OPE coefficients in three-dimensional superconformal field theories with ${\cal N} \geq 4$ supersymmetry. These relations follow from a consistent truncation of the…

High Energy Physics - Theory · Physics 2014-12-30 Shai M. Chester , Jaehoon Lee , Silviu S. Pufu , Ran Yacoby

Partial Differential Equations (PDE) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the behaviour of natural and engineered systems. In…

We propose some multigrid methods for solving the algebraic systems resulting from finite element approximations of space fractional partial differential equations (SFPDEs). It is shown that our multigrid methods are optimal, which means…

Numerical Analysis · Mathematics 2018-07-27 Yingjun Jiang , Xuejun Xu

This paper proposes some efficient and accurate adaptive two-grid (ATG) finite element algorithms for linear and nonlinear partial differential equations (PDEs). The main idea of these algorithms is to utilize the solutions on the $k$-th…

Numerical Analysis · Mathematics 2020-09-22 Yukun Li , Yi Zhang

Physical processes evolving in both time and space are often modeled using Partial Differential Equations (PDEs). Recently, it has been shown how stability analysis and control of coupled PDEs in a single spatial variable can be more…

Analysis of PDEs · Mathematics 2026-05-20 Declan S. Jagt , Matthew M. Peet

Conformal field theory (CFT) plays a key role in modern theoretical physics. Through CFT we describe real physical systems at criticality and fixed points of the renormalization group flow. It is also central in the study of quantum…

High Energy Physics - Theory · Physics 2024-04-19 Giulia Peveri

In multi-phase fluid flow, fluid-structure interaction, and other applications, partial differential equations (PDEs) often arise with discontinuous coefficients and singular sources (e.g., Dirac delta functions). These complexities arise…

Numerical Analysis · Mathematics 2019-07-24 Chung-Nan Tzou , Samuel Stechmann

In this work, we present a machine learning approach for reducing the error when numerically solving time-dependent partial differential equations (PDE). We use a fully convolutional LSTM network to exploit the spatiotemporal dynamics of…

Machine Learning · Computer Science 2020-02-11 Ben Stevens , Tim Colonius