Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs.

翻译：(生物)物理系统动力学模型的参数推断仍然是一个具有挑战性的问题。在缺乏大量计算资源的情况下，难以处理的梯度、高维空间以及非线性模型函数通常都会带来困难。该领域近期的相关工作主要聚焦于贝叶斯推断方法，这些方法从统计分布角度考虑参数，因此无法得出最优参数值的点估计。本文提出了一种新的元启发式算法，通过特征信息驱动的降维（DR-FFIT）来解决上述瓶颈。DR-FFIT实现了一种高效的采样策略，便于在高维空间中进行无梯度参数搜索。我们利用人工神经网络为模型的特征目标获取可微分的代理模型。由此产生的梯度使得我们能够在定义的采样区域内估计模型的局部活性子空间。该方法能以较低的计算成本实现高度非线性搜索空间的高效降维。我们的测试数据表明，DR-FFIT能在可控的运行时间成本内，提升随机搜索和模拟退火算法相较于现有成熟元启发式算法的性能，并改善模型的拟合优度。