We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the Helmholtz energy, we adopt the internal energy and a dissipation potential as primary constitutive functions, expressed in terms of deformation and entropy. This choice avoids the need to enforce mixed convexity--concavity conditions and facilitates a consistent incorporation of thermodynamic principles. In this contribution, we focus on materials without preferred directions or internal variables. While the formulation is posed in terms of entropy, the temperature is treated as the independent observable, and the entropy is inferred internally through the constitutive relation, enabling thermodynamically consistent modeling without requiring entropy data. Thermodynamic admissibility of the networks is guaranteed by construction. The internal energy and dissipation potential are represented by input convex neural networks, ensuring convexity and compliance with the second law. Objectivity, material symmetry, and normalization are embedded directly into the architecture through invariant-based representations and zero-anchored formulations. We demonstrate the performance of the proposed framework on synthetic and experimental datasets, including purely thermal problems and fully coupled thermomechanical responses of soft tissues and filled rubbers. The results show that the learned models accurately capture the underlying constitutive behavior. All code, data, and trained models are made publicly available via https://doi.org/10.5281/zenodo.19248596.

翻译：我们提出了一种基于物理的神经网络框架，用于在全耦合热力学中本构模型的发现。与基于亥姆霍兹能的经典公式不同，我们采用内能和耗散势作为主要本构函数，并以变形和熵为变量表述。这一选择避免了强制执行混合凸-凹条件，并促进了热力学原理的一致性融入。在本研究中，我们重点关注无优选方向或内变量的材料。尽管公式以熵为基础，但温度被视为独立可观测变量，而熵则通过本构关系内部推断，从而在无需熵数据的情况下实现热力学一致性建模。神经网络通过构造保证热力学可容许性。内能和耗散势由输入凸神经网络表示，确保凸性及对第二定律的遵从。客观性、材料对称性和归一化通过基于不变量的表示和零基准公式直接嵌入网络架构中。我们展示了该框架在合成数据与实验数据集上的性能，包括纯热问题以及软组织和填充橡胶的全耦合热力学响应。结果表明，所学模型能够准确捕捉潜在的本构行为。所有代码、数据和训练模型均通过 https://doi.org/10.5281/zenodo.19248596 公开提供。