Lijun Yang

3 weeks ago | nature.com | Zeyu Li |Wang Han |Yimin Hou |Hongjue Li |Zhiguo Jiang |Lijun Yang | +2 more

Identifying governing equations from observational data is crucial for understanding nonlinear physical systems but remains challenging due to the risk of overfitting. Here we introduce the Bi-Level Identification of Equations (BILLIE) framework, which simultaneously discovers and validates equations using a hierarchical optimization strategy. The policy gradient algorithm of reinforcement learning is leveraged to achieve the bi-level optimization. We demonstrate BILLIE’s superior performance through comparisons with baseline methods in canonical nonlinear systems such as turbulent flows and three-body systems. Furthermore, we apply the BILLIE framework to discover RNA and protein velocity equations directly from single-cell sequencing data. The equations identified by BILLIE outperform empirical models in predicting cellular differentiation states, underscoring BILLIE’s potential to reveal fundamental physical laws across a wide range of scientific fields. In this study the authors introduce BILLIE, a bi-level framework for equation identification that decouples term selection and quantification, enhancing robustness and accuracy in modeling nonlinear systems. BILLIE outperforms existing methods in handling complex systems and imperfect data, as demonstrated through both simulations and real-world biological applications.

Dec 3, 2024 | mdpi.com | Lijun Yang |Yi Wang

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Jun 3, 2024 | link.aps.org | Lijun Yang |Sichuan Normal

The non-Hermitian interaction in many-body systems may lead to novel phenomena. We study an impurity immersed in three-dimensional ideal Fermi gases, with a complex-valued contact interaction between the fermions and impurity that arise from two-body dissipation. Generalizing the unified variational approach to the non-Hermitian case, we study the effects of the dissipative attraction on the polaronic and molecular states of the impurity for different mass ratios.

Jun 1, 2023 | pubs.rsc.org | Lijun Yang |Yi Tian |Yutong Wang |Wei Qi

Improving the catalytic ability of peptide-based artificial glycosidase through tyrosine strategy Lijun Yang,   Yi Tian,   Yutong Wang,   Wei Qi  and  Mengfan Wang   Abstract Recently, peptide-based artificial enzymes have attracted growing interests due to the similar composition of peptide assemblies with natural enzymes. However, low catalytic activity and stability are still the main challenges for these enzyme-like catalysts.