We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

翻译：我们提出了一种热力学计算机的设计方案，该计算机能够在平衡或非平衡状态下执行任意非线性计算。简单的热力学电路——即与热浴接触并被四次势所约束的涨落自由度——所表现出的活动性是其输入的非线性函数。因此，这类电路可被视为热力学神经元，并可作为网络化结构的基本单元，从而构成热力学神经网络。这种网络作为通用函数逼近器，其运行由热涨落驱动。我们模拟了一个热力学神经网络的数字模型，并证明其参数可以通过遗传算法进行调整，以在指定的观测时间执行非线性计算，无论系统是否已达到热平衡。这项工作将热力学计算领域扩展到了热平衡区域之外，使得在指定观测时间进行完全非线性的计算成为可能，其功能类似于经典神经网络所执行的计算。