The ongoing and next generation of cosmological surveys, including DESI, CSST, Euclid, LSST, and the Roman Space Telescope, will provide unprecedented measurements of the large-scale structure of the Universe. Fully exploiting these datasets requires accurate and efficient theoretical predictions capable of modeling nonlinear structure formation, galaxy bias, and astrophysical processes across a wide range of scales. High-resolution numerical simulations provide the most powerful framework for this purpose, but their computational cost limits direct exploration of high-dimensional cosmological and astrophysical parameter spaces. Recent advances in simulation emulators, machine learning-based surrogate models, and forward modeling techniques offer new opportunities to bridge the gap between expensive simulations and cosmological inference. These developments enable rapid predictions not only for traditional summary statistics, but also for high-dimensional observables such as cosmic fields, weak-lensing maps, and galaxy catalogs.
Simulation-based inference (SBI) and modern machine learning methods are further expanding the scope of cosmological analysis beyond conventional likelihood-based approaches. Field-level inference aims to extract information directly from continuous cosmic fields, while catalog-level inference provides a complementary approach by utilizing the detailed information encoded in galaxy distributions and galaxy–halo connections. Combining these approaches with accurate simulation emulators and realistic forward models offers a promising pathway toward maximizing the scientific return of current and future surveys. This workshop will bring together researchers working on cosmological simulations, large-scale structure, galaxy formation, machine learning, and statistical inference to discuss recent progress and future challenges in emulator development, field- and catalog-level inference, uncertainty quantification, and simulation-driven cosmology.
Invited Speakers:
TBD
SOC:
Yin Li (Pengcheng Lab)
Yu Yu (SJTU)
Zhongxu Zhai (SJTU)
Pengjie Zhang (SJTU)