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Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Qingliang Fan, Ruike Wu, Yanrong Yang
https://arxiv.org/abs/2507.16433 https://…

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of factor modeling, we propose a novel data-adaptive multi-task learning methodology that quantifies and learns the relatedness among the principal temporal subspaces (spanned by factors) across multiple sectors under study. This approach not only improves the …

This https://arxiv.org/abs/2408.13266 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Impact of Climate transition on Credit portfolio's loss with stochastic collateral
The aim of this work is to propose an end-by-end modeling framework to evaluate the risk measures of a bank's portfolio of collateralized loans in an economy subject to the climate transition. The economy, organized in sectors, is driven by a multidimensional Ornstein-Uhlenbeck (OU) productivity process while the climate transition is declined thanks to continuous deterministic carbon price and intensities processes. We thus derive the dynamics of macroeconomic variables for each scenario. By c…

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Shahab Azarfar, Joseph B. Choi, Phong CH. Nguyen, Yen T. Nguyen, Pradeep Seshadri, H. S. Udaykumar, Stephen Baek
https://arxiv.org/abs/2506.12996

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (subgrid) meso-scale dynamics is upscaled to the homogenized (macro-scale) representation of the heterogeneous material through closure models. Deep learning models trained using meso-scale simulation data are now a popular route to assimilate such closure laws. However, meso-scale simulations are computationally taxing,…