UAP Reverse Engineering Study

Multi-pipeline materials analysis of unidentified aerial phenomenon fragments through independent cross-validated methodologies.

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Our Mission

Our mission is to advance materials science through rigorous multi-method analysis of unknown material samples. By developing new analytical frameworks that combine AI classification, archival comparison, and structured perception protocols, we aim to set a new standard for characterizing materials of uncertain origin with scientific rigor and full reproducibility.

Project Goal

The goal is to complete comprehensive characterization of all submitted fragments, publish findings through peer-reviewed channels, and establish a replicable multi-pipeline analysis methodology. Success means delivering cross-validated material profiles with anomaly detection across all three independent analytical pipelines.

Who We Are?

We are an interdisciplinary research group operating within the Advanced Scientific Research Projects (ASRP) holding — an independent association of scientific and technological initiatives implementing projects at the intersection of artificial intelligence, materials science, biomedicine, and cognitive sciences.The project is led by Valeriia Ovsyannikova — Director of the Department of Biomedical Research and Chief Biomedical Engineer at ASRP. She provides strategic and operational leadership for the project, covering the full cycle of reverse engineering: analysis, reconstruction, interpretation, and formalization of data. Her area of responsibility also includes hardware implementation and experimental reproduction of technological solutions, including prototype development, functional emulation, and lab-scale reconstruction of observed properties and behaviors of the systems under study.The choice of a biomedical leadership profile is driven by the characteristics of the objects under study: preliminary results indicate the presence of complex hybrid materials exhibiting signs of deep biotechnological integration. These include biohybrid adaptive systems, metallobiological composites, and self-organizing structural matrices that demonstrate properties characteristic of living systems — including adaptivity, response to external stimuli, and signs of functional regulation.Such materials may be classified as systems at the boundary between living and non-living matter, requiring integrative methodologies that combine biomedicine, biophysics, synthetic biology, materials science, and complex systems theory. Within the project, engineering replication pipelines are being developed, aimed at restoring the functional principles and technological patterns underlying the observed objects.Ivan Saveliev provides scientific support for the project, including preparation and editing of scientific publications, structuring and formalization of results, methodological verification, and alignment of research with international academic standards.The research employs a multi-level data classification system (PUBLIC → RESTRICTED → CONFIDENTIAL → CLASSIFIED), ensuring rigorous control, verification, and phased disclosure of information as it undergoes scientific processing, reproducibility, and confirmation.

Element 115 & UAP: Device Architecture

In this video, Valeriia Ovsyannikova (Co-Founder & Chief Biomedical Engineer of ASRP) explores the hypothesis on the potential role of Moscovium (Element 115) in UAP technologies. The discussion covers its proposed operating principle as an energy source and gravity control mechanism — including directional gravitational field generation, inertia compensation, propellantless propulsion, and the concept of a stable isotope. This research is part of ASRP's global UAP technology reverse engineering project, conceptually aligned with programs like the Advanced Aerospace Threat Identification Program (AATIP). The material is exploratory in nature and focuses on analyzing physical principles that extend beyond the current scientific paradigm.

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Analytical Pipelines

AI Visual & Material Analysis:

Description:

This pipeline deploys four neural architectures — ResNet50, ViT-B/16, EfficientNet-B4, and a custom Autoencoder — for structural pattern recognition and material classification. Samples are categorized across metallic, ceramic, polymeric, and exotic metamaterial taxonomies, with 3D surface reconstruction generating point clouds of up to one million points from multi-view imagery.

Archival & Comparative Analysis:

Description:

Fragment characteristics are systematically cross-referenced against established material databases and historical case records through pattern matching algorithms. This pipeline provides an independent empirical baseline that operates entirely without AI inference, ensuring that comparative conclusions rest on direct observational data.

Extended Cognitive Perception:

Description:

Controlled observer sessions graded L1 through L4 produce structured perception reports that are processed via semantic vector analysis for quantitative comparison. This methodology transforms subjective observations into measurable data and is connected to the broader ASRP patent portfolio.