**3. Challenges**

Despite its many benefits, numerical simulation has its limitations. Models simplify real-world systems and are only as accurate as the assumptions and approximations made in their development. Furthermore, simulations can be computationally expensive, and the results can be sensitive to the numerical methods and algorithms used in the simulation.

Another challenge is the need for better validation and verification of simulations. Because simulations are based on mathematical models, validating and verifying experimentally can be challenging. As a result, researchers must rely on a combination of physical experiments, empirical data, and mathematical analysis to ensure that their simulations are accurate and reliable [8].

There are also some ethical concerns associated with its use. For example, using simulations to design autonomous weapons raises questions about the morality of using machines to make life-or-death decisions. Similarly, using simulations to model the behavior of financial markets raises questions about the ethics of using algorithms to make decisions that affect people's lives and livelihoods.

Another concern is the potential for simulations to perpetuate bias and discrimination. Models are only as accurate as the data they are trained on. The generated simulations may perpetuate those biases and inequalities if that data contains preferences or reflects societal disparities. As such, it is crucial for researchers to be aware of the potential for bias in their simulations and to take steps to mitigate it.

There is also a concern that the increasing reliance on numerical simulation may lead to a loss of intuition and creativity in scientific and engineering research. For example, as researchers become increasingly reliant on simulations to generate

#### *Introductory Chapter: Numerical Simulation DOI: http://dx.doi.org/10.5772/intechopen.112097*

predictions and test theories, there is a risk that they may need help to think outside the box and come up with novel ideas [9].

As the use of numerical simulation continues to grow, researchers need to remain vigilant about these tools' potential risks and limitations. Models are inherently simplifications of reality, and simulations can be sensitive to the assumptions and approximations made in their development. As such, it is essential for researchers to validate their models and simulations using experimental data and to continuously refine their models and algorithms to ensure accuracy and reliability [10].

Moreover, increasing complexity and size of simulations can present significant computational challenges, requiring large-scale parallel computing resources and specialized software. As such, it is important for researchers to have access to these resources and to develop strategies for efficient and scalable simulations.
