Data-Driven Forward and Inverse Modeling of V-Beam Thermal Sensors

2026-07-04

AI Summary

This study addresses the ill-posed inverse design problem of V-beam thermal actuators, where multiple geometric and material configurations can yield the same target displacement at a given temperature. To overcome this non-uniqueness, the authors propose a data-driven two-stage optimization framework. First, a neural network forward model is trained to predict thermo-mechanical responses from geometric and material parameters. This model is then frozen and embedded within a gradient-based inverse optimization loop that simultaneously minimizes structural volume and mechanical stress. The approach effectively circumvents the failure of direct regression in ill-conditioned inverse problems and enables efficient multi-objective geometric optimization. Evaluated on a dataset of 3,000 samples, the forward model achieves a mean absolute percentage error (MAPE) of 4.76% in displacement prediction, with over 70% of samples exhibiting errors below 5%.

Abstract

This paper presents a machine learning framework for data-driven inverse design of V-beam thermal sensors. The goal is to determine the optimal sensor geometry: beam inclination angle, beam length and beam width that achieves a target displacement under a given temperature. The design should also provide the geometry with minimum structure volume and minimum mechanical stress the sensor must support. This problem is ill-posed as for a given displacement there are multiple possible geometric configurations, causing direct regression methods to fail. We document a series of five exploratory trials that progressively revealed the nature of the problem culminating in a two-phase solution: a neural network forward model trained to map geometry and material constants to sensor responses, a gradient-descent inverse optimization over the frozen forward model, minimizing stress and volume simultaneously. The proposed pipeline utilizes a 3000-sample dataset and achieves a MAPE of 4.76% for predicting the displacement, more than 70% of predictions having MAPE of under 5%.

Problem

• inverse design
• thermal sensors
• ill-posed problem
• geometry optimization
• multi-objective optimization

Innovation

• inverse design
• neural network forward model
• gradient-based optimization
• thermal sensor
• data-driven modeling