What are the most effective computational modeling techniques for scientific research?

I work in the field of infectious disease modeling using the One Health approach. Computational modeling is as true and effective as taking into context all the considerations (social, environmental, economic, livelihood, and human behavioral issues). The whole-of-society approach in developing a model will make it realistic and fit for purpose. Such a model should be scalable, tried first in small spatial dimensions, and scaled up where necessary. That said, the model choice is based on the issues at hand, what fits best, and how applicable it is to the current challenge.

The most effective computational modelling techniques for scientific research are the simplest ones, yet achieving the required accuracy. If applicable, a suitable lower-dimensional model will always be much more efficient than its higher-dimensional counterpart.

In my point of view and as per my research domain (Material science and Electronic devices), I found this to be an effective computational modeling technique, 1. Deep Learning and Neural Networks (Like ANN, DNN, SNN. etc) 2. Computational Chemistry (Like DFT) 3. Mathematical Optimization.

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DEMs can be very useful to get a different perspective. At the same model can be challenging due to inherent complexity. Moreover, DEMs can very well model complex systems as it considers the physical laws governing the system for loss function and training.

These are widely used in physics, engineering, and many natural sciences to describe continuous processes and relationships. They are suitable for systems with well-understood mathematical principles.

Differential Equations (DEs) can be used to model systems where an object/phenomenon change continuously in time and/or space. Some common examples of DE models are neurons in the brain circuits, evolution of population of species, spread of infectious diseases in a population, etc. However, a major challenge is that not all of DEs are solvable. Even numerical techniques are available only for a few specific equations. Therefore, researchers will have to depend on qualitative analysis of the systems which are useful depending on the application. Sensitivity analysis plays key role in studying the spread of a disease. Bifurcations are useful in understanding the neuron's spiking behaviour. Stability analysis is useful in study of population.

I found Differential Equation Models (DEM) indispensable in research. DEMs use mathematical equations to represent change over time, allowing us to forecast health trends and outcomes based on current data. Their strength lies in their ability to model continuous changes, making them valuable for studying disease spread and population health dynamics. In Bangladesh, DEMs can provide insights into how diseases progress in communities, helping shape public health interventions. Yet, they require precise data and expertise in mathematical modelling. Despite these challenges, using DEMs offers a promising avenue to enhance healthcare planning and response in Bangladesh.

As a writer with 30 years experience through various mediums, plus interviews, I find this to be totally infallible. "What goes round, comes around."

One of the standout features of MLMs is their capacity to capture the inherent nonlinearity, complexity, and variability found in many real-world systems. They excel at discerning subtle patterns, uncovering hidden features, and optimizing performance. Moreover, they can generalize their learned knowledge to make predictions on new, unseen data. However, MLMs also come with some inherent challenges. They can often behave as 'black-boxes,' making it difficult to interpret their decision-making processes. They are prone to overfitting, meaning they may perform exceptionally well on the training data but struggle to generalize effectively to new data.

ML can be applied to various domains when you have large datasets and want to discover patterns, make predictions, or classify data. It's versatile and effective in tasks like image recognition, natural language processing, and more.

I have used many ML methods to process medical data (tabular, image, signal). There is no best, and all are useful. I will introduce only a few algorithms: 1- Radiomics: a set of algorithms for extracting features from an image (including radiological images) 2- XGBoost: a popular and efficient ML algorithm for classification (and maybe regression as well) that is an implementation of the gradient boosted trees algorithm 3- Mask R-CNN: a Convolutional Neural Network (CNN) and state-of-the-art in terms of image segmentation and object detection.

As a Clinical researcher, I've learned and used Machine Learning (ML) models in research. ML models utilize algorithms to sift through vast datasets, discern patterns, and make predictions. Their adaptability suggests various healthcare applications, from diagnostics to personalized treatment plans. Within the Bangladesh context, while the potential of ML models to transform patient care is evident, there's a significant challenge: many of us need to be made aware of its capabilities or lack the know-how to implement it effectively. This gap underscores the need for training and infrastructure. Despite our current limitations, understanding and adopting ML could be a game-changer for healthcare in Bangladesh.

Let me say, philosophically physics is the law of nature and mathematics is a tool to comprehend it. As researchers we try to factor all the variables that may describe the physical phenomena, but it is not possible to do so , but we try to try to better describe and understand it. So, I have developing mathematical models desribing describing different physical systems (governed by PDE's, ODE's , Using Machine Learning) , however I try to simplify the models using Linear algebra (for DE's, if possible Ex:Concept FEA methods). I have used ML techniques when needed ; however in my humble opinion trying to find the right tool for the problem statement is an effective solution. The most efficient computational tecnique is problem specific.

These are useful for modeling and analyzing relationships between entities, such as social networks, transportation networks, and disease spread. Graph theory and network science techniques are often used.

They are useful for studying the structures of high dimensional systems where other approaches like Cartesian planes would be too convoluted/obfuscating. For example, the study of protein space, where each dimension is given by amino acids in the protein chain, is potentially well suited for the application of this model.

As a Bangladeshi clinical researcher, I value Network Models (NMs) for their ability to represent systems as interconnected nodes and links, offering insights into relational dynamics in areas like social networks and transportation systems. NMs capture topology, centrality, and connectivity, aiding in understanding network properties, pinpointing influential nodes, and simulating dynamics. Yet, they come with challenges, especially in Bangladesh. They're data-intensive, sometimes neglect node attributes and behaviours, and might need to be more accurate to make connections. While NMs present a promising avenue for in-depth study, careful consideration is essential to harness their full potential and avoid pitfalls.

Network models (NMs) are a valuable tool for molecular biology researchers, particularly in the fields of bioinformatics, phylogeny, and evolution. NMs can be used to represent the complex interactions between genes, proteins, organisms, and ecosystems. By analyzing NMs, researchers can gain insights into the structure and function of biological systems. For example, NMs can be used to identify key genes that are involved in disease processes, or to reconstruct the evolutionary history of a species. However, it is important to be aware of the limitations of NMs. As with any computational model, NMs are a simplification of reality. They can only capture a limited amount of information about a system.

When we analyze the dynamics of a complex system (remember DEM's usually wont work) through its time series, the effects of interactions are to some extent hidden or averaged. Networks are mathematical objects that, on one hand, explicitly consider interactions and, moreover, admit multiple isomorphisms and/or homomorphisms. We have been using them for modeling ecological interactions of gut microbiota and asses this ecosystem health in terms of network connectivity. Most interestingly the same approach works to asses the brain activity network, so we can start to understand coupled dynamics of microbiota-gut-brain axis. Using bayesian networks one can understand key aspects by studying its probabilistic structure.

For me it will have to be the following: 1) Need / Complexity 2) Data Available (Quality) 3) Budget (Current costs and maintenance) 4) Validation of Model 5) Scalability I have seen in most cases when projects are taken up, organizations are not clear of what they want to derive. They know the essence of the output but do not know what specific outputs they are expecting out of the model. Another important aspect is Data. Whether it is data being trained on or the data which is used for testing. Quality is key. Also, different types of models have different strengths and weaknesses. It's important to consider these carefully when making your decision of which model to pick up.

Artificial intelligence( AI) is a promising methodology of the current times, with the capability of incorporating human behaviour and intelligence into machines or systems and so should be useful in scientific research. Artificial intelligence based modeling is the key in making automated, intelligent, and smart systems which are designed keeping in mind the present requirements. Resolving real-world issues, different types of AI's like logical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of any research.

In scientific research, there's no one-size-fits-all model. Your choice depends on factors like your research question, domain knowledge, data access, computational resources, and model purpose. To decide, define your research goal, assess system components and interactions, review existing models, compare techniques, and pick what aligns best with your project's needs and feasibility.

Model can be choose depending on the process involved in a processes. Sometimes, model also can be selected depending on the duration of process. Therefore, appropriate model selection often reduces huge error during handling a modeeling.

Network models (NMs) are computational tools for analyzing complex systems such as social and transportation, as well as biological networks. NMs excel at capturing network structure, including topology, node centrality, and connectivity. They're used to measure network properties and simulate dynamic changes. However, NMs have limitations. They require comprehensive, high-quality data, which can be challenging. They may prioritize structural aspects over individual node attributes and behaviors. Additionally, they can oversimplify link interactions. In sum, NMs are valuable for studying network structures, but their suitability depends on data quality, the relevance of node attributes, and network complexity.

I'm sorry to be very critical, but this page seems to me to be of extremely limited interest. There is no such thing as "most effective computational modeling techniques for scientific research". A model is a tool for solving particular problems, and, even if it is true that we can identify a certain number of main categories, we must think of a plurality of models that are complementary to each other: the versatile tool capable of doing everything well is fictional, or ultimately very limited, just as the Swiss Army knife has never replaced the carpenter's or mechanic's toolbox.

The premise that some computational modeling techniques are inherently more effective than others is misleading. Each modeling technique's effectiveness is intertwined with the context, research question, and nature of the system being studied. To claim superiority of one over the other neglects the multifaceted nature of scientific research. In essence, it's counterproductive to evaluate computational models in isolation or in comparison without context. It's akin to debating whether a hammer is more effective than a screwdriver; the answer depends on whether you're dealing with a nail or a screw.

To choose a model: 1. Define Your Research Question: Clearly state your research objectives and the problem you want to address. 2. Understand the Nature of Your Data: Consider the type and amount of data you have. Is it discrete or continuous? Structured or unstructured? 3. Assess Assumptions and Data Availability: Some models may require specific assumptions about the system, while others might need extensive data. Ensure that you have the necessary input for your chosen model. 4. Consider Model Complexity: Choose a model that matches the complexity of your problem. Simpler models may be sufficient for some questions, while others may require more complex approaches then validate and test:

Even when one has rich data sets and powerful compute tools at hand, I’ve often found it useful to also add more qualitative study methods as a way of clarifying the ‘why’ of theorized cause and effect, and as a way to clarify how one may implement study findings in practice.

In my experience, we need to look at any model or all models which we work with, as parts to solve a problem. There is no perfect model. These(models) are all tools which can help us create better solutions. The creative dimension would be to use all of these effectively, since research itself is a deep domain and when you add the intricacies of science to it, it becomes more complex.