Suchergebnisse - "Krishnapriyan, Aditi"

Investigating the behavior of diffusion models for accelerating electronic structure calculations.

Autoren: Rothchild, Daniel Rosen, Andrew S. Taw, Eric et al.

Quelle: Chemical Science; 9/7/2024, Vol. 15 Issue 33, p13506-13522, 17p

Datasets and Distillation Labels for the Paper 'Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians'

Autoren: Amin, Ishan Raja, Sanjeev Krishnapriyan, Aditi

Weitere Verfasser: Amin, Ishan Raja, Sanjeev Krishnapriyan, Aditi

Schlagwörter: Machine Learning, Molecular and chemical physics, Materials Science, Quantum chemistry

Relation: https://zenodo.org/records/14759305; oai:zenodo.org:14759305; https://doi.org/10.5281/zenodo.14759305

Verfügbarkeit: https://doi.org/10.5281/zenodo.14759305
https://zenodo.org/records/14759305

Topological Regularization via Persistence-Sensitive Optimization

Autoren: Nigmetov, Arnur Krishnapriyan, Aditi S. Sanderson, Nicole et al.

Index Begriffe: Computer Science - Machine Learning, Mathematics - Algebraic Topology, text

URL: http://arxiv.org/abs/2011.05290

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Autoren: Swenson, Nicolas Krishnapriyan, Aditi S. Buluc, Aydin et al.

Index Begriffe: Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Mathematics - Algebraic Topology, text

URL: http://arxiv.org/abs/2010.16027

Learning continuous models for continuous physics.

Autoren: Krishnapriyan, Aditi S. Queiruga, Alejandro F. Erichson, N. Benjamin et al.

Quelle: Communications Physics; 11/3/2023, Vol. 6 Issue 1, p1-14, 14p

Schlagwörter: MACHINE learning, ENGINEERING models, NUMERICAL analysis, DYNAMICAL systems, EXTRAPOLATION, SYSTEM dynamics, PHYSICS

Comparative Analysis of Physics-Informed Neural Networks Using TOPSIS: Balancing Efficiency, Interpretability, and Physical Law Integration.

Autoren: Prasanth, Vidhya Poonkodi, Sathiyamoorthy Mohan, Anusuya et al.

Quelle: REST Journal on Advances in Mechanical Engineering; Sep2025, Vol. 4 Issue 3, p59-72, 14p

Schlagwörter: TOPSIS method, MACHINE learning, MECHANICAL efficiency, SCIENTIFIC computing

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Autoren: Krishnapriyan, Aditi S. Montoya, Joseph Haranczyk, Maciej et al.

Quelle: Scientific Reports; 4/26/2021, Vol. 11 Issue 1, p1-11, 11p

Schlagwörter: MACHINE learning, NATURAL language processing, CARBON dioxide, ADSORPTION (Chemistry), HOMOLOGY (Biochemistry)

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows.

Autoren: Fuchi, Kazuko W. Wolf, Eric M. Makhija, David S. et al.

Quelle: International Journal of Computational Fluid Dynamics; Aug2022, Vol. 36 Issue 7, p618-640, 23p

Schlagwörter: FLUID flow, MACHINE learning, STEADY-state flow, INCOMPRESSIBLE flow, BOUNDARY value problems, CARTESIAN coordinates