We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods. Our PyTorch implementation is available at https://github.com/jiajunhe98/FEAT.

翻译：我们提出了基于自适应传输的自由能估计方法（FEAT），这是一种用于自由能估计的新框架——该问题是跨科学领域的关键挑战。FEAT通过随机插值器实现学习型传输，基于伴随Jarzynski等式与受控Crooks定理提供一致的最小方差估计器，同时给出自由能差分的变分上界与下界。FEAT将平衡态与非平衡态方法统一于单一理论框架下，为神经自由能计算建立了原理性基础。在玩具模型、分子模拟和量子场论中的实验验证表明，该方法优于现有的基于学习的方法。我们的PyTorch实现可在https://github.com/jiajunhe98/FEAT获取。