Thermophysical properties of fluid mixtures are important in many fields of science and
engineering. However, experimental data are scarce in this field, so prediction methods are …

We present the first hard-constraint neural network model for predicting activity coefficients
(HANNA), a thermodynamic mixture property that is the basis for many applications in …

The physics-informed neural network (PINN) gravity model offers a novel and efficient way to
represent high-fidelity gravity fields. PINNs leverage modern deep learning strategies to …

Asteroids' and comets' geodesy is a challenging yet important task for planetary science and
spacecraft operations, such as ESA's Hera mission tasked to look at the aftermath of the …

The application of physics-informed neural networks (PINNs) is investigated for the first time
in solving the one-dimensional countercurrent spontaneous imbibition (COUCSI) problem at …

Current physics-informed (standard or deep operator) neural networks still rely on accurately
learning the initial and/or boundary conditions of the system of differential equations they are …

The high-frequency gravity forward modeling relying on the residual terrain modeling (RTM)
technique is essential for gravity data processing, fine gravity field modeling, geophysical …

In this work, we develop computationally efficient methods for deterministic production
optimization under nonlinear constraints using a kernel-based machine learning method …

Recent advances in modeling density distributions, so-called neural density fields, can
accurately describe the density distribution of celestial bodies without, eg, requiring a shape …

In the framework of Space Traffic Management (STM) the growing number of space debris
and the consequent increased risk of collisions have become a topic of great interest …