In this special issue, we propose a regularization data base to measurements. The objective is continuum simulations from partial differential equations towards precision across fields of automobiles, sensors, bio sensors, visualization, devices and engineering measurements and technology. The machine learning, data sciences, neural networks added to user defined functions having partial differential equations with matching providing sensitivity, repeatability, uncertainty quantification and geometry optimization to precise experiments only in the rapidly evolving physics informed neural networks. The predict of results from .stl files equivalent are rapid towards Artificial Intelligence hardware equivalent from electronics, memristors, fluid computers and neuromorphic computers. The advancements of the equivalence theorem from physics informed neural networks flow chart dynamics towards the machine provides the advancements in the special issue.

The overview of the topic significance is that the use of continuum simulations with neural networks towards applications are foreseen in .stl based data machine learning of automobiles in large scales, geometries and design variants. The topic is expandable to match the sensors in precision, design optimization in automobiles, sustainable, energy, bioengineering, robotics and heavy duty machines.

The relevance of machining towards object in automobiles, sustainable, energy, bioengineering, robotics and heavy duty machines in academic context with Ansys software match will result in new computer technology software and hardware.

The primary goal of this special issue is to collect articles in machine learning, data sciences, artificial intelligence, neural networks and physics informed neural networks in measurements for bio and non bio engineering and technology applications. We invite contributions that explore measurements for bio and non bio engineering, with particular interest in machine learning and neural networks.

We welcome the following types of articles: Original research articles, review articles, case studies and ethics.

Through this special issue, we aim to achieve papers that are readable for measurements and advancements in new age neural network hardware computers. We welcome researchers from various disciplines to provide interdisciplinary perspectives on Physics informed neural network and continuum simulations to measurements. Your contributions will play a crucial role in advancing knowledge in this field.