joint-surrogate-learning-neuromorphic

name: joint-surrogate-learning-neuromorphic description: DMOSOPT — scalable optimization framework using jointly learned surrogate models for constrained multi-objective optimization of neural dynamical systems. Learns smooth approximations of objective landscapes and feasibility boundaries to guide search with unified gradients. version: 1.0.0 metadata: hermes: tags: [multi-objective-optimization, surrogate-models, DMOSOPT, neural-dynamics, supercomputing, constrained-optimization] source_paper: "Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems (arXiv:2603.20984)" citations: 0

DMOSOPT: Joint Surrogate Learning for Neural System Optimization

Overview

Paper: arXiv:2603.20984 (2026-03-22) Authors: Gressmann, Frithjof; Raikov, Ivan Georgiev; Kim, Seung Hyun; Gazzola, Mattia; Rauchwerger, Lawrence

Biophysical neural system simulations are among the most computationally demanding scientific applications. Their optimization requires navigating high-dimensional parameter spaces under numerous constraints with binary feasible/infeasible partitions and no gradient signal. DMOSOPT introduces a unified, jointly learned surrogate model that captures the interplay between objectives, constraints, and parameter sensitivities.

Key Contributions

1. Unified Joint Surrogate — Single model learns objectives, constraints, and sensitivities simultaneously
2. Smooth Approximation — Learns smooth approximations of objective landscapes and feasibility boundaries
3. Unified Gradient Steering — Provides gradients that simultaneously steer toward better objectives and constraint satisfaction
4. Sensitivity Estimation — Partial derivatives yield per-parameter sensitivity estimates for targeted exploration
5. Supercomputing Scale — Validated from single-cell dynamics to population-level networks at scale

Problem Setting

Challenge

• High-Dimensional Parameter Spaces: Neural models have many tunable parameters
• Binary Constraints: Feasible/infeasible regions with no gradient signal
• Computational Cost: Each simulation evaluation is expensive
• Multi-Objective: Multiple competing objectives to optimize simultaneously

DMOSOPT Solution

Training Data → Joint Surrogate Model → Unified Gradient + Sensitivities → Guided Optimization → Fewer Evaluations

Joint Surrogate Model

Components

1. Objective Surrogate: Smooth approximation of the objective function landscape
2. Constraint Surrogate: Smooth approximation of the feasibility boundary
3. Sensitivity Estimator: Partial derivatives provide per-parameter importance

Unified Gradient

The joint surrogate provides a single gradient signal that:

• Steers toward improvement: Direction of objective optimization
• Respects constraints: Avoids infeasible regions
• Guides exploration: Sensitivity estimates focus search on impactful parameters

Optimization Pipeline

1. Initial Sampling: Generate initial parameter configurations (e.g., Latin hypercube)
2. Evaluation: Run expensive simulations for each configuration
3. Surrogate Training: Fit joint surrogate model to collected data
4. Gradient-Guided Search: Use surrogate gradients to propose new configurations
5. Iterative Refinement: Alternate between evaluation and surrogate updates
6. Convergence: Stop when improvement plateaus or budget exhausted

Validation Scope

• Single-Cell Dynamics: Ion channel parameter optimization
• Population-Level Networks: Network connectivity and dynamics tuning
• Incremental Stages: Full neural circuit modeling workflow
• Supercomputing Scale: Validated on HPC systems

Applications

• Computational Neuroscience: Parameter fitting for biophysical models
• Neuromorphic Computing: Hardware parameter optimization
• Scientific Computing: Constrained multi-objective optimization in any domain
• Hyperparameter Optimization: ML model tuning with expensive evaluations

When to Use This Skill

• Optimizing expensive simulation-based models with constraints
• Multi-objective optimization where gradient information is unavailable
• Parameter fitting for biophysical neural models
• Any scenario with expensive evaluations and complex feasibility constraints

Advantages Over Traditional Methods

References

• Paper: Gressmann, F., Raikov, I.G., Kim, S.H., Gazzola, M., Rauchwerger, L. "Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems," arXiv:2603.20984, Mar. 2026
• Related: Surrogate modeling, multi-objective optimization, Bayesian optimization, parameter fitting