Engineers at the University of Pennsylvania have developed an AI technique using 'mollifier layers' to solve complex inverse partial differential equations more efficiently and with greater stability.

Penn researchers have developed a smarter AI method for solving notoriously difficult inverse equations, which help ...

Researchers at the University of Pennsylvania have solved a persistent obstacle in computational mathematics: how to reliably ...

Inverse problems in differential equations constitute a pivotal area in applied mathematics and engineering, where the aim is to deduce unknown parameters or inputs within a differential equation from ...

In this graph, 𝑥 and 𝑦 are directly proportional. This means that when 𝑥 doubles, 𝑦 also doubles, and when 𝑥 triples, so does 𝑦. In fact, for all coordinates on this line, you could multiply or ...

Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly ...

Inverse problems in fractional differential equations encompass the challenging task of deducing unknown parameters, source terms or initial conditions from observed data in systems governed by ...