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We present a new approach for evaluating Feynman integrals numerically. Weapply the recently-proposed framework of physics-informed deep learning totrain neural networks to approximate the solution to the differential equationssatisfied by the Feynman integrals. This approach relies neither on a canonicalform of the differential equations, which is often a bottleneck for theanalytical techniques, nor on the availability of a large dataset, and aftertraining yields essentially instantaneous evaluation times. We provide aproof-of-concept implementation within the PyTorch framework, and apply it to anumber of one- and two-loop examples, achieving a mean magnitude of relativedifference of around 1% at two loops in the physical phase space with networktraining times on the order of an hour on a laptop GPU.

Learning Feynman integrals from differential equations with neural networks