13 papers
Neural Spectral Bias and Conformal Correlators II: Modular and Annulus Bootstrap
Kausik Ghosh, Sidhaarth Kumar, Vasilis Niarchos +1
We develop a neural network bootstrap framework for reconstructing partition functions of two-dimensional conformal field theories (CFTs) based on modular invariance and the Cardy…
Neural Networks Reveal a Universal Bias in Conformal Correlators
Kausik Ghosh, Sidhaarth Kumar, Vasilis Niarchos +1
The paper shows that simple neural networks trained on crossing symmetry can accurately reconstruct conformal correlators from minimal input, suggesting a universal bias that can b…
Thermal Double-Twist Data in Holography
V. Niarchos, C. Papageorgakis, A. Stratoudakis
We explain how to extract thermal OPE coefficients of double-twist operators in scalar two-point functions at infinite spatial volume from suitably regularized integrals of thermal…
Efficient Conformal Block Evaluation with GoBlocks
James Chryssanthacopoulos, Vasilis Niarchos, Constantinos Papageorgakis +1
Conformal blocks in odd spacetime dimensions are not known in closed analytic form. To facilitate efficient computations in the conformal bootstrap, we introduce $\texttt{GoBlocks}…
Neural Networks, Dispersion Relations and the Thermal Bootstrap
Vasilis Niarchos, Constantinos Papageorgakis
We review a framework for the conformal bootstrap that does not rely on positivity and treats the infinite tower of high-dimension OPE contributions to conformal correlators throug…
Neural Operators as Efficient Function Interpolators
Vasilis Niarchos, Angelos Sirbu, Sokratis Trifinopoulos
Neural operators (NOs) are designed to learn maps between infinite-dimensional function spaces. We propose a novel reframing of their use. By introducing an auxiliary base-space, a…