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

• URL: http://arxiv.org/abs/2607.09752v1
• Date: Sat, 04 Jul 2026 12:50:19 GMT
• Title: Data-Driven Forward and Inverse Modeling of V-Beam Thermal Sensors
• Abstract summary: This paper presents a machine learning framework for data-driven inverse design of V-beam thermal sensors.<n>The goal is to determine the optimal sensor geometry that achieves a target displacement under a given temperature.<n>The proposed pipeline utilizes a 3000-sample dataset and achieves a MAPE of 4.76% for predicting the displacement.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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