Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems

2026-03-21

AI Summary

This work addresses the challenges of optimizing high-dimensional, heavily constrained neural dynamical systems—specifically, the binary partitioning of feasible regions and the absence of gradient signals—by introducing the DMOSOPT framework. DMOSOPT uniquely integrates the objective function, constraints, and parameter sensitivities into a unified surrogate model and constructs a differentiable feasibility boundary via smooth approximation, enabling gradient-driven simultaneous optimization. The approach substantially reduces the number of simulation evaluations required across scales, from single-cell to population-level network models, and efficiently solves highly constrained neural dynamical system optimization problems at supercomputing scale, thereby supporting multiscale scientific computing.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Biophysical neural system simulations are among the most computationally demanding scientific applications, and their optimization requires navigating high-dimensional parameter spaces under numerous constraints that impose a binary feasible/infeasible partition with no gradient signal to guide the search. Here, we introduce DMOSOPT, a scalable optimization framework that leverages a unified, jointly learned surrogate model to capture the interplay between objectives, constraints, and parameter sensitivities. By learning a smooth approximation of both the objective landscape and the feasibility boundary, the joint surrogate provides a unified gradient that simultaneously steers the search toward improved objective values and greater constraint satisfaction, while its partial derivatives yield per-parameter sensitivity estimates that enable more targeted exploration. We validate the framework from single-cell dynamics to population-level network activity, spanning incremental stages of a neural circuit modeling workflow, and demonstrate efficient, effective optimization of highly constrained problems at supercomputing scale with substantially fewer problem evaluations. While motivated by and demonstrated in the context of computational neuroscience, the framework is general and applicable to constrained multi-objective optimization problems across scientific and engineering domains.

Problem

• multi-objective optimization
• constrained optimization
• neural dynamical systems
• high-dimensional parameter spaces
• feasibility boundary

Innovation

• joint surrogate learning
• multi-objective optimization
• constraint handling
• parameter sensitivity
• neural dynamical systems