  The development of data-driven approaches for solving differential equations
has been followed by a plethora of applications in science and engineering
across a multitude of disciplines and remains a central focus of active
scientific inquiry. However, a large body of natural phenomena incorporates
memory effects that are best described via fractional integro-differential
equations (FIDEs), in which the integral or differential operators accept
non-integer orders. Addressing the challenges posed by nonlinear FIDEs is a
recognized difficulty, necessitating the application of generic methods with
immediate practical relevance. This work introduces the Universal Fractional
Integro-Differential Equation Solvers (UniFIDES), a comprehensive machine
learning platform designed to expeditiously solve a variety of FIDEs in both
forward and inverse directions, without the need for ad hoc manipulation of the
equations. The effectiveness of UniFIDES is demonstrated through a collection
of integer-order and fractional problems in science and engineering. Our
results highlight UniFIDES' ability to accurately solve a wide spectrum of
integro-differential equations and offer the prospect of using machine learning
platforms universally for discovering and describing dynamical and complex
systems.
