The Effective Geometry Monte Carlo Algorithm: Applications to Molecular Communication. - Fatih Dinc; Leander Thiele; Bayram Cevdet Akdeniz (CoRR, 2018)

Effective Geometry Monte Carlo: A Fast and Reliable Simulation Framework for Molecular Communication. - Fatih Dinc; Matija Medvidovic; Leander Thiele (IEEE Access, 2019)

Multifield Cosmology with Artificial Intelligence. - Francisco Villaescusa-Navarro; Daniel Anglés-Alcázar; Shy Genel; David N. Spergel; Yin Li 009; Benjamin D. Wandelt; Andrina Nicola; Leander Thiele; Sultan Hassan; José Manuel Zorrilla Matilla; Desika Narayanan; Romeel Dave; et al. (CoRR, 2021)

Robust marginalization of baryonic effects for cosmological inference at the field level. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Angles-Alcazar; David N. Spergel; Yin Li 009; Benjamin D. Wandelt; Leander Thiele; Andrina Nicola; José Manuel Zorrilla Matilla; Helen Shao; Sultan Hassan; Desika Narayanan; et al. (CoRR, 2021)

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Angles-Alcazar; Leander Thiele; Romeel Dave; Desika Narayanan; Andrina Nicola; Yin Li 009; Pablo Villanueva-Domingo; Benjamin D. Wandelt; David N. Spergel; Rachel S. Somerville; et al. (CoRR, 2021)

The CAMELS project: public data release. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Anglés-Alcázar; Lucia A. Perez; Pablo Villanueva-Domingo; Digvijay Wadekar; Helen Shao; Faizan G. Mohammad; Sultan Hassan; Emily Moser; Erwin T. Lau; Luis Fernando Machado Poletti Valle; et al. (CoRR, 2022)

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. - Digvijay Wadekar; Leander Thiele; Francisco Villaescusa-Navarro; J. Colin Hill; David N. Spergel; Miles D. Cranmer; Nicholas Battaglia; Daniel Anglés-Alcázar; Lars Hernquist; Shirley Ho (CoRR, 2022)

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. - Digvijay Wadekar; Leander Thiele; J. Colin Hill; Shivam Pandey; Francisco Villaescusa-Navarro; David N. Spergel; Miles D. Cranmer; Daisuke Nagai; Daniel Anglés-Alcázar; Shirley Ho; Lars Hernquist (CoRR, 2022)

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. - Leander Thiele; Miles D. Cranmer; William R. Coulton; Shirley Ho; David N. Spergel (Mach. Learn. Sci. Technol., 2022)

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI. - Leander Thiele; Adrian E. Bayer; Naoya Takeishi (CoRR, 2025)

Set-based Implicit Likelihood Inference of Galaxy Cluster Mass. - Bonny Y. Wang; Leander Thiele (CoRR, 2025)