workflow Li/Bai added

workflow_Tiankai_Li_Haogeng_Bai.ipynb

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+    "# Computational Foundations of Digital Twin Technologies"
+   ]
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+    "# Bayesian Active Learning of Model Parameters to Increase Predictive Quality in Digital Twins of Flow-like Geohazards Main work packages\n",
+    "\n",
+    "## 1. Introduction to Bayesian Active Learning and Geohazard Prediction\n",
+    "- Overview of Bayesian active learning.\n",
+    "- Relevance in geohazard prediction and digital twin modeling.\n",
+    "\n",
+    "## 2. Deep Dive into the Article\n",
+    "- Analysis of Bayesian inference, Gaussian process emulation, active learning in the context of the article.\n",
+    "- Discussion on computational challenges and solutions.\n",
+    "\n",
+    "## 3. Practical Application and Tool Familiarization\n",
+    "- Hands-on session with PSimPy: Run predefined examples.\n",
+    "- Integration of Bayesian active learning in landslide modeling.\n",
+    "\n",
+    "## 4. Data Analysis and Model Calibration\n",
+    "- Replication of study scenarios from the article.\n",
+    "- Comparative analysis of posteriors for different observations.\n",
+    "- Impact of data error and observational types on model accuracy.\n",
+    "\n",
+    "## 5. Discussion and Critical Analysis\n",
+    "- Findings in the context of the article.\n",
+    "- Evaluation of Bayesian active learning approach effectiveness and limitations.\n",
+    "\n",
+    "\n",
+    "# Presentation on Bayesian Active Learning in Geohazard Prediction\n",
+    "\n",
+    "## Introduction\n",
+    "- Brief overview of Bayesian Active Learning.\n",
+    "- Its significance in geohazard prediction and modeling.\n",
+    "\n",
+    "## Article Insights\n",
+    "- Key concepts from the article on computational challenges in geohazard prediction.\n",
+    "- Solutions offered by Bayesian approaches.\n",
+    "\n",
+    "## Practical Session with PSimPy\n",
+    "- Demonstration of PSimPy for landslide modeling.\n",
+    "- Implementation of Bayesian Active Learning in practical scenarios.\n",
+    "\n",
+    "## Data Analysis and Results\n",
+    "- Analysis of different observational data types.\n",
+    "- Impact on model accuracy and predictions.\n",
+    "\n",
+    "## Critical Discussion\n",
+    "- Effectiveness and limitations of Bayesian Active Learning.\n",
+    "- Examples and insights from the article.\n",
+    "\n",
+    "## Conclusion\n",
+    "- Summary of findings.\n",
+    "- Future applications and potential advancements.\n",
+    "\n",
+    "## Q&A Session\n",
+    "- Open discussion and clarification of concepts.\n",
+    "\n",
+    "\n"
+   ]
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