The central question in artificial intelligence today is no longer simply whether an algorithm can learn from data. A more intriguing question has emerged: Can an algorithm learn while incorporating prior knowledge of the physical laws that govern the world? This question introduces the concept of Physics-Informed Artificial Intelligence (Physics-Informed AI), an emerging approach that combines the power of machine learning with the physical knowledge used to describe the behavior of real-world systems. Instead of training a model on thousands or millions of data points and allowing it to identify relationships independently, this approach incorporates physical laws directly into the learning process. These may include the conservation of mass, energy, and momentum, as well as equations governing heat transfer, fluid dynamics, electromagnetism, and other physical phenomena. In this way, algorithms are trained not only to recognize what has happened but also to account for the physical constraints that determine what can happen. From Data Alone to Data and Physical Laws Traditional artificial intelligence models rely heavily on data. In general, the greater the diversity and quality of the available data, the better a model can learn to make predictions. However, in engineering and scientific applications, large datasets are not always readily available. Conducting physical experiments can be expensive, while high-fidelity numerical simulations may require hours, days, or even longer when investigating complex systems. Furthermore, models trained exclusively on data may occasionally produce predictions that appear statistically reasonable but are physically impossible. This leads to the fundamental idea behind Physics-Informed AI: Why should an algorithm learn physical principles entirely from scratch when substantial knowledge of those principles is already available? For example, when predicting temperature distributions within a thermal system, a model can be trained not only to match experimental measurements but also to satisfy the governing heat transfer equation and the system’s boundary conditions. Through this approach, physics is transformed from external information used to evaluate predictions after training into an integral component of the learning process itself. Physics-Informed Neural Networks One of the most widely recognized implementations of this approach is known as Physics-Informed Neural Networks (PINNs). In conventional neural networks, model performance is typically evaluated by measuring the difference between predicted outputs and actual values in the training dataset. In PINNs, additional terms are introduced into the loss function to represent how closely the model satisfies the governing physical equations. For instance, if a system is governed by a partial differential equation, the training process penalizes predictions that fail to satisfy that equation within an acceptable level of accuracy. As a result, model training integrates three essential elements: available data, governing physical laws, and the boundary or initial conditions of the system. The intended outcome is a model that seeks not only numerical agreement with the observed data but also greater consistency with the constraints imposed by physics. Why Do Engineers Need Physics-Informed AI? The importance of Physics-Informed AI lies in its potential to bridge the gap between conventional numerical simulation and artificial intelligence. Many engineering problems depend on complex mathematical models. Studying airflow around an aircraft wing, heat transfer through a heat exchanger, stress distribution within a structure, or fluid movement inside a medical device often requires computationally expensive numerical solutions. Physics-Informed AI can support the development of surrogate models capable of approximating these solutions rapidly after appropriate training and validation. This does not necessarily eliminate the need for conventional numerical methods, such as the Finite Volume Method (FVM), the Finite Element Method (FEM), or Computational Fluid Dynamics (CFD) tools. Instead, traditional simulations can provide high-quality training data, while physical constraints help improve the consistency of the intelligent model’s predictions with actual system behavior. Consequently, in certain applications, engineers may move from conducting time-consuming simulations for every new scenario toward using trained models that rapidly estimate system responses under a wide range of operating conditions. When Physics Becomes Part of Intelligence The significance of this approach extends beyond computational speed. It also changes how researchers understand the relationship between scientific knowledge and artificial intelligence. Data-driven AI can identify patterns and relationships that researchers may not have previously recognized. Physics, meanwhile, provides a framework that helps prevent models from generating results that contradict established scientific knowledge. This creates an important complementary relationship: Data tells the model what has been observed, while physics informs it about what is possible. By combining both sources of knowledge, the model may become better equipped to handle situations that were not explicitly represented during training, particularly when the governing physical laws are well understood and the application conditions remain consistent with the model’s assumptions. Applications Beyond the Laboratory Physics-Informed AI has applications across a broad range of engineering and scientific disciplines. In fluid dynamics and heat transfer, it can be used to estimate velocity, pressure, and temperature fields and accelerate certain simulation processes. In energy engineering, it can support the development of fast predictive models for evaluating the thermal performance of batteries, solar cells, thermal energy storage systems, and heat exchangers. In biomedical engineering, it can assist in simulating blood flow, heat transfer through biological tissues, and physical phenomena associated with medical devices. In civil and mechanical engineering, the governing laws of deformation, stress, and vibration can be incorporated into machine learning models to predict structural behavior or support structural health monitoring. Growing research interest also extends to climate modeling, geophysical systems, electrochemical engineering, robotics, and digital twin technologies. Making Digital Twins More Intelligent One area that may particularly benefit from Physics-Informed AI is Digital Twin technology. A digital twin requires more than a virtual representation of a physical device or system. It also needs the ability to update its state continuously using information received from sensors. Physics-informed models can combine real-world measurements with the governing physical laws of a system, helping create digital representations capable of estimating future operating conditions and identifying potential deviations or problems before they develop into actual failures. In this way, digital twins can evolve beyond passive monitoring tools into systems that support prediction and informed decision-making. Can Artificial Intelligence Replace Physics? The most realistic answer is no. The strength of Physics-Informed AI comes precisely from its ability to incorporate physical knowledge rather than replace it. Artificial intelligence can deliver rapid predictions after training, but it depends on reliable data and accurate scientific knowledge. Traditional physics-based models, on the other hand, provide explanations of system behavior but may require substantial computational resources when dealing with large-scale systems or repeated optimization processes. Therefore, the most promising future is not necessarily a competition between artificial intelligence and physical simulation, but rather the development of hybrid systems that combine their complementary strengths. Challenges Along the Way Despite its considerable potential, Physics-Informed AI is not a universal solution to every engineering problem. Training physics-informed neural networks can become particularly challenging when dealing with highly nonlinear equations, multiscale phenomena, or complex turbulent systems. Another important difficulty involves balancing different components of the training loss function, particularly those associated with observed data and governing physical equations. Furthermore, if the physical knowledge incorporated into the model is incomplete or excessively simplified, the limitations of those assumptions may also affect the resulting predictions. Developing reliable models therefore requires expertise in two interconnected areas: artificial intelligence and the physical system being investigated. From Black-Box AI to Scientific Intelligence One of the most distinctive aspects of Physics-Informed AI is its potential to transform artificial intelligence from a purely statistical black-box approach into a scientific tool informed by established laws and prior knowledge. The ultimate objective may not be to create algorithms that understand physics in the same way humans do, but rather to develop learning models whose behavior is more consistent with the physical laws governing natural systems. This transformation could become an important foundation for the next generation of digital engineering. Instead of choosing between mathematical equations and data, researchers can use both. Instead of treating physics and artificial intelligence as separate disciplines, they can integrate them into a unified framework designed to understand and predict real-world behavior more efficiently and reliably. Conclusion Physics-Informed AI represents an important step toward developing engineering and scientific models that combine the computational capabilities of machine learning with the established foundations of physical laws. Rather than replacing scientific knowledge, this approach uses it to guide learning and improve the ability of models to produce results that are more consistent with physical reality. This is where the most compelling idea behind Physics-Informed AI becomes clear: Algorithms do not merely learn from data; they learn within a world governed by the laws of physics. Al-Mustaqbal University The First University in Iraq