The following pages link to Getting CICY high (Q2273830):
Displaying 13 items.
- Deep learning and k-means clustering in heterotic string vacua with line bundles (Q779316) (← links)
- Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning (Q2047897) (← links)
- Algorithmically solving the tadpole problem (Q2067210) (← links)
- Hilbert series, machine learning, and applications to physics (Q2119631) (← links)
- Data science applications to string theory (Q2187812) (← links)
- A systematic approach to Kähler moduli stabilisation (Q2225714) (← links)
- Contrast data mining for the MSSM from strings (Q2230191) (← links)
- Machine learning Calabi-Yau four-folds (Q2232628) (← links)
- Machine learning Lie structures \& applications to physics (Q2233703) (← links)
- Estimating Calabi-Yau hypersurface and triangulation counts with equation learners (Q2421176) (← links)
- Neural network approximations for Calabi-Yau metrics (Q2678044) (← links)
- Towards the “Shape” of Cosmological Observables and the String Theory Landscape with Topological Data Analysis (Q5153519) (← links)
- Black holes and the loss landscape in machine learning (Q6061784) (← links)
