  Additive manufacturing has been recognized as an industrial technological
revolution for manufacturing, which allows fabrication of materials with
complex three-dimensional (3D) structures directly from computer-aided design
models. The mechanical properties of interpenetrating phase composites (IPCs),
especially response to dynamic loading, highly depend on their 3D structures.
In general, for each specified structural design, it could take hours or days
to perform either finite element analysis (FEA) or experiments to test the
mechanical response of IPCs to a given dynamic load. To accelerate the
physics-based prediction of mechanical properties of IPCs for various
structural designs, we employ a deep neural operator (DNO) to learn the
transient response of IPCs under dynamic loading as surrogate of physics-based
FEA models. We consider a 3D IPC beam formed by two metals with a ratio of
Young's modulus of 2.7, wherein random blocks of constituent materials are used
to demonstrate the generality and robustness of the DNO model. To obtain FEA
results of IPC properties, 5,000 random time-dependent strain loads generated
by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction
forces and stress fields inside the IPC beam under various loading are
collected. Subsequently, the DNO model is trained using an incremental learning
method with sequence-to-sequence training implemented in JAX, leading to a 100X
speedup compared to widely used vanilla deep operator network models. After an
offline training, the DNO model can act as surrogate of physics-based FEA to
predict the transient mechanical response in terms of reaction force and stress
distribution of the IPCs to various strain loads in one second at an accuracy
of 98%. Also, the learned operator is able to provide extended prediction of
the IPC beam subject to longer random strain loads at a reasonably well
accuracy.
