Pan Kessel

Physics and Astronomy · Physical Sciences

14h-index1.3kcitations50works0.92yr avg
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Research Topics

Model Reduction and Neural Networks(9), Theoretical and Computational Physics(8), Explainable Artificial Intelligence (XAI)(7), Neural Networks and Applications(6), Hydrocarbon exploration and reservoir analysis(6)

Publications50 total

Conformation-aware structure prediction of antigen-recognizing immune proteins
mAbs·2025· 2 citedOpen Access
Do we need equivariant models for molecule generation?
arXiv (Cornell University)·2025Open Access
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
arXiv (Cornell University)·2025Open Access
Equivariant Neural Tangent Kernels
arXiv (Cornell University)·2024Open Access
Fast and Unified Path Gradient Estimators for Normalizing Flows
arXiv (Cornell University)·2024· 1 citedOpen Access
Emergent Equivariance in Deep Ensembles
arXiv (Cornell University)·2024Open Access
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
arXiv (Cornell University)·2024· 3 citedOpen Access
NeuLat: a toolbox for neural samplers in lattice field theories
2023· 1 citedOpen Access
Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories
Physical review. D/Physical review. D.·2023· 23 citedOpen Access
Diffeomorphic Counterfactuals With Generative Models
IEEE Transactions on Pattern Analysis and Machine Intelligence·2023· 13 citedOpen Access
Generalization within in silico screening
arXiv (Cornell University)·2023Open Access
Learning trivializing gradient flows for lattice gauge theories
Physical review. D/Physical review. D.·2023· 31 citedOpen Access
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
arXiv (Cornell University)·2023· 1 citedOpen Access
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows
Machine Learning Science and Technology·2022· 14 citedOpen Access
Author response for "Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL divergence for Normalizing Flows"
2022
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
arXiv (Cornell University)·2022· 1 citedOpen Access
Path-Gradient Estimators for Continuous Normalizing Flows
arXiv (Cornell University)·2022· 7 citedOpen Access
Diffeomorphic Counterfactuals with Generative Models
arXiv (Cornell University)·2022Open Access
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021)·2022· 19 citedOpen Access
Deep learning for surrogate modeling of two-dimensional mantle convection
2022· 1 citedOpen Access
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Frequent Co-authors

Kim A. Nicoli(16), Christopher J. Anders(13), Shinichi Nakajima(13), Klaus‐Robert Müller(11), Siddhant Agarwal(10), Nicola Tosi(10), D. Breuer(10), Ann-Kathrin Dombrowski(9), Karl Jansen(7), Lorenz Vaitl(7), Grégoire Montavon(7), Tobias Hartung(6), Lena Funcke(5), Jan E. Gerken(5), Sebastiano Padovan(5), Paolo Stornati(4), Nils Strodthoff(3), Wojciech Samek(3), Andrew M. Watkins(3), Kristof T. Schütt(2)