My first scientific paper - Revision history

Wiki: /* My first scientific paper */

2026-02-25T20:03:18Z

My first scientific paper

Wiki: /* Causal AI Models for Spatial-Time Series, 2025 */

2026-02-08T13:11:56Z

Causal AI Models for Spatial-Time Series, 2025

Wiki: /* See also */

2026-02-08T13:10:15Z

Wiki at 11:36, 6 February 2026

2026-02-06T11:36:50Z

Wiki: /* Foundation AI Models for Science, 2025 */

2025-09-09T11:10:36Z

Foundation AI Models for Science, 2025

Wiki: /* Functional Data Analysis, 2025 */

2025-07-02T14:02:42Z

Functional Data Analysis, 2025

Wiki: /* Functional Data Analysis, 2025 */

2025-07-02T13:59:05Z

Functional Data Analysis, 2025

Wiki: /* Functional Data Analysis, 2025 */

2025-07-02T13:58:41Z

Functional Data Analysis, 2025

Wiki: /* My first scientific paper */

2025-06-02T14:14:52Z

My first scientific paper

Wiki at 13:58, 2 June 2025

2025-06-02T13:58:48Z

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 ===Links===
 * 2025 results [https://github.com/intsystems/m1p/tree/main-2025 GitHub]
 * 2025 [https://github.com/intsystems/m1p/tree/main-2025 The list of problems for 2025]

@@ Line 34: / Line 34: @@
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
-==Causal AI Models for Spatial-Time Series, 2025==
+==Causal AI Models for Spatial-Time Series==
-'''Foundation AI models''' are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series.
+'''Foundation AI models''' are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series. See the [[Functional Data Analysis]] page.
 ==Mathematical forecasting, 2026==

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 * [[Step 11|Step 11: The final talk]]
-==See also==
+==Articles==
-* [[Fundamental theorems]] of ML
+* [[Fundamental theorems]] of Machine Learning
 * [https://m1p.org/jmlda JMLDA archive]<!--|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]-->

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-{{#seo: |title=Research management course|titlemode=append|keywords=Research management course|description=This research management course immerses students in research activities that produce scientific papers}}
+{{#seo: |title=AI for applied scientific research|titlemode=append|keywords=Machine Learning, Signal processing, Quantum computing, Causal Inference|description=This research management course immerses students in research activities that produce scientific papers with code}}
 [[File:Miai logo1.jpeg|class=img-responsive|left|alt=My first scientific paper|link=Course_schedule]] &nbsp;
 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
 == My first scientific paper ==
-This course produces student research papers. It gathers research teams. Each team consists of a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research activities. The graduate student consultant conducts the research and helps the student. The expert, a professor, states the problem and enlightens the way to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
+This course produces student research papers. It gathers research teams. Each team consists of a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research. The graduate student consultant conducts their research and helps. The expert, a professor, states the problem and enlightens the way to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
 *[[Week 0|Week 0: Sign up]]
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 * [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
 == The Art of Scientific Research ==

@@ Line 4: / Line 4: @@
 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
-==Foundation AI Models for Science, 2025==
+==Foundation AI Models for Spatial-Time Series, 2025==
 '''Foundation AI models''' are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series.
 [[Functional Data Analysis|See the FDA page]].

@@ Line 5: / Line 5: @@
 ==Functional Data Analysis, 2025==
-'''Foundational models for science'''. Foundation AI models are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series.
+'''Foundation AI models''' are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series.
 [[Functional Data Analysis|See the FDA page]].

@@ Line 25: / Line 25: @@
 *[[Week 10|Week 10: Select a journal to submit]]
 *[[Week 11|Week 11: Prepare your presentation]]
-*[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)]
+*[https://www.youtube.com/watch?v=uwcbMJamBbM Week 12: Show your results (Youtube)]
 ===Links===

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 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
-==Foundational models for science, 2025==
+==Functional Data Analysis, 2025==
-The statistical analysis of spatial time series requires additional methods of data analysis. First,  we suppose time is continuous, put the state space changes <math>\frac{d\mathbf{x}}{dt}</math>, and use neural ordinary and stochastic differential equations. Second, we analyze a multivariate and multidimensional time series and use the tensor representation and tensor analysis. Third, since the time series have significant cross-correlation, we model them in the Riemannian space. Fourth, medical time series are periodic, the base model is the pendulum model, <math>\frac{d^2x}{dt^2}=-c\sin{x}</math>. We use physics-informed neural networks to approximate data. Fifth, the practical experiments involve multiple data sources. We use canonical correlation analysis with a latent state space. This space aligns the source and target spaces and generates data in the source and target manifolds. [[Functional Data Analysis|See the FDA page]].
+'''Foundational models for science'''. The statistical analysis of spatial time series requires additional methods of data analysis. First,  we suppose time is continuous, put the state space changes <math>\frac{d\mathbf{x}}{dt}</math>, and use neural ordinary and stochastic differential equations. Second, we analyze a multivariate and multidimensional time series and use the tensor representation and tensor analysis. Third, since the time series have significant cross-correlation, we model them in the Riemannian space. Fourth, medical time series are periodic, the base model is the pendulum model, <math>\frac{d^2x}{dt^2}=-c\sin{x}</math>. We use physics-informed neural networks to approximate data. Fifth, the practical experiments involve multiple data sources. We use canonical correlation analysis with a latent state space. This space aligns the source and target spaces and generates data in the source and target manifolds. [[Functional Data Analysis|See the FDA page]].
 ==Mathematical forecasting, 2025==