ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Physics-informed neural networks for highly compressible flows 🧠🌊

Collection of how and why software systems fail

RMC-TotalRisk is a powerful risk analysis software package designed to support dam and levee safety investment decisions.

[UAI 2023] Official implementation of Efficient Failure Pattern Identification of Predictive Algorithms

Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty

Enabling Model-Based Diagnosis and Failure Model Generation with Active Automata Learning

Empirical benchmark comparing agent architectures (single-agent, multi-agent, adaptive) on ProgramDev-v0 and CyberGym tasks. Key finding: adaptive architecture > single-agent > fixed-pipeline multi-agent.

What breaks after an AI agent team keeps running, and the runtime layer needed to recover.

PressureX is an engineering evaluation package for a passive layered structural mitigation concept using shear-thickening fluid behavior to broaden impulsive loads and reduce peak transmitted response in high-vibration aerospace structures. Targets are design-intent until validated.

Source-available, measurement-first pulsed-energy testbed for tri-sector storage/discharge control, derated storage, phase authority, sensor-truth checks, energy accounting, kill criteria, evidence bundles, and human-reviewed scale-up gates.

Architecture for resilient, governed, and regenerative intelligence under uncertainty.

Runtime-agnostic hook harness that catches unverifiable prompts, enforces failure-mode templates, and gates task completion on passing tests.

A practical library of failure modes in AI workflows, automation, and production systems.

Computational implementation for design methods of cellular steel beams

A systems-first implementation of agent control: explicit retrieval decisions, planner–executor separation, and auditable memory as core architectural mechanisms.

Minimal core theory of fluctuation and membrane as the boundary condition for self, world, meaning, and AI safety.

A failure mode taxonomy for RAG-based clinical AI, organized by where in the clinical documentation and retrieval lifecycle the failure originates. v1.0 reference implementation.

A systems-level analysis of static RAG pipelines, isolating ingestion, retrieval, and ranking boundaries to expose structural failure modes before generation.