Hallucination Risk in Large Language Models
“Recent studies have shown that the percentage of hallucinated content is quite high among popular LLMs, ranging from 17% to 19% up to 45% of the content. If left without serious attention and the appropriate corrections, AI hallucinations can lead to critical limitations of AI applications that negatively impact human civilization and its progress.”
Environmental Efficiency and Ethical AI Usage
Research published in Nature demonstrates that AI systems can contribute to a significant reduction in CO2 emissions. This environmental benefit represents a crucial advancement in sustainable technology deployment.
We strongly advocate for the responsible use of AI-generated content. Our tools are designed to assist researchers and writers in refining their original work, rather than replacing human creativity and critical thinking.
Panda v3.0 Detection Model
Advanced neural architecture optimized for LLM-generated content detection.
Core Detection Features
Accuracy
0.95
F1 Score
0.96
Backed by Research
Predictive Maintenance
We combine artificial intelligence and machine learning techniques with mathematical approaches to monitor equipment conditions in real time and predict failures before they occur.
Examples from Our Previous Work
One of the key aspects of modern Prognostics and Health Management studies is engine component failure prediction, which aims to predict the development of degradation before malfunction or operational failure occurs. Correct calculation of the Remaining Useful Life of key engine components improves safety, reduces maintenance costs, and supports the shift toward predictive maintenance paradigms in the energy and aerospace industries.
Recent studies have inclined towards deep learning-based prognostic models, capable of extracting and learning representations directly from raw sensor data. Hybrid approaches that combine domain knowledge with sequence models continue to improve predictive fidelity and support deployable industrial monitoring systems.
Scientific Simulations
The idea behind it is simple: conduct multiple simulations at different input levels and then average the resulting output, then train a neural network to approximate the functional relationship between inputs and outputs. When trained, this neural network can be used as a fast surrogate, giving rough answers in a few seconds instead of the hours that traditional simulation might take.
Physics-Informed Neural Networks (PINNs)
One of the most popular methods is the Physics-Informed Neural Network. Compared to purely data-driven networks, PINNs use physical laws as constraints during training, encoding concepts like conservation of energy or fluid dynamics so predictions remain consistent with known physics.
Surrogate Modeling
Another approach is surrogate modeling. A computationally expensive simulator generates a large sample of input-output pairs, which are then used to fit a smaller fast surrogate model. The surrogate allows quick exploration of millions of design variants and can be beneficial to optimization studies.
Molecular & Materials Science
In chemistry and materials science, machine-learning models are used to predict interatomic interactions. Instead of calculating the properties of each constituent atom via ab initio methods, the neural network learns emergent patterns, allowing systems with millions of atoms to be simulated simultaneously.
Reliability Concerns
There is a main issue of reliability: such models can be overconfident and misleading, especially in scenarios far outside the distribution of their training data. As a result, AI is usually used by scientists as a convenience to speed up computational processes rather than replace established simulation methods outright.
Example - Airplane Wing Shape Optimization
A practical application of surrogate-based simulation is optimizing airplane wing geometries for minimal drag and maximum lift efficiency. Traditional CFD simulations of airflow around a wing can take hours per design variant. By training a neural surrogate on thousands of CFD runs, engineers can evaluate millions of wing shapes in real time and identify optimal profiles.
Scientific Projects
AI-Powered Geological Monitoring
We integrate artificial intelligence with autonomous drone systems to monitor landslides, seismic activity, and terrain instability, delivering real-time insights faster and more affordably than traditional methods.
Our geotechnology capabilities include AI-powered landslide detection, continuous seismic monitoring, high-resolution 3D terrain mapping, automated risk assessment, and scalable regional monitoring networks.
