research
∙
11/17/2022
Machine Learned Calabi–Yau Metrics and Curvature
Finding Ricci-flat (Calabi–Yau) metrics is a long standing problem in ge...
share
research
∙
09/19/2022
Towards Quantum Advantage on Noisy Quantum Computers
Topological data analysis (TDA) is a powerful technique for extracting c...
research
∙
02/15/2022
Identifying equivalent Calabi–Yau topologies: A discrete challenge from math and physics for machine learning
We review briefly the characteristic topological data of Calabi–Yau thre...
research
∙
01/27/2022
Efficient Quantum Computation of the Fermionic Boundary Operator
The boundary operator is a linear operator that acts on a collection of ...
research
∙
12/16/2021
Machine Learning Kreuzer–Skarke Calabi–Yau Threefolds
Using a fully connected feedforward neural network we study topological ...
research
∙
11/30/2021
Learning knot invariants across dimensions
We use deep neural networks to machine learn correlations between knot i...
research
∙
12/31/2020
Neural Network Approximations for Calabi-Yau Metrics
Ricci flat metrics for Calabi-Yau threefolds are not known analytically....
research
∙
12/07/2020
Disentangling a Deep Learned Volume Formula
We present a simple phenomenological formula which approximates the hype...
research
∙
03/23/2020
Baryons from Mesons: A Machine Learning Perspective
Quantum chromodynamics (QCD) is the theory of the strong interaction. Th...
research
∙
02/06/2019
Restriction enzymes use a 24 dimensional coding space to recognize 6 base long DNA sequences
Restriction enzymes recognize and bind to specific sequences on invading...
research
∙
06/27/2018
hep-th
We apply techniques in natural language processing, computational lingui...
research
∙
06/08/2018
