Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj	(revision 16494)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj	(revision 16495)
@@ -169,4 +169,9 @@
     <Compile Include="Regression\PennML\PennMLRegressionDataDescriptor.cs" />
     <Compile Include="Regression\PennML\PennMLRegressionInstanceProvider.cs" />
+    <Compile Include="Regression\Physics\AircraftLift.cs" />
+    <Compile Include="Regression\Physics\AircraftMaximumLift.cs" />
+    <Compile Include="Regression\Physics\FluidDynamics.cs" />
+    <Compile Include="Regression\Physics\PhysicsInstanceProvider.cs" />
+    <Compile Include="Regression\Physics\RocketFuelFlow.cs" />
     <Compile Include="Regression\VariableNetworks\LinearVariableNetwork.cs" />
     <Compile Include="Regression\VariableNetworks\GaussianProcessVariableNetwork.cs" />
Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftLift.cs
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftLift.cs	(revision 16495)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftLift.cs	(revision 16495)
@@ -0,0 +1,94 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class AircraftLift : ArtificialRegressionDataDescriptor {
+    public override string Name { get { return "Aircraft Lift Coefficient C_L = C_La (a - a0) + C_Ld_e d_e S_HT / S_ref"; } }
+
+    public override string Description {
+      get {
+        return "A full description of this problem instance is given in: " + Environment.NewLine +
+          "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " +
+          "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " +
+          "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine +
+          "Function: C_L = C_La (a - a0) + C_Ld_e d_e S_HT / S_ref" + Environment.NewLine +
+          "with C_La ∈ [0.4, 0.8]," + Environment.NewLine +
+          "a ∈ [5°, 10°]," + Environment.NewLine +
+          "C_Ld_e ∈ [0.4, 0.8]," + Environment.NewLine +
+          "d_e ∈ [5°, 10°]," + Environment.NewLine +
+          "S_HT ∈ [1m², 1.5m²]," + Environment.NewLine +
+          "S_ref ∈ [5m², 7m²]," + Environment.NewLine +
+          "a0 is set to -2°";
+      }
+    }
+
+    protected override string TargetVariable { get { return "C_L"; } }
+    protected override string[] VariableNames { get { return new string[] { "C_La", "a", "a0", "C_Ld_e", "d_e", "S_HT", "C_L" }; } }
+    protected override string[] AllowedInputVariables { get { return new string[] { "C_La", "a", "a0", "C_Ld_e", "d_e", "S_HT" }; } }
+    protected override int TrainingPartitionStart { get { return 0; } }
+    protected override int TrainingPartitionEnd { get { return 100; } }
+    protected override int TestPartitionStart { get { return 100; } }
+    protected override int TestPartitionEnd { get { return 200; } }
+
+    public int Seed { get; private set; }
+
+    public AircraftLift() : this((int)System.DateTime.Now.Ticks) { }
+
+    public AircraftLift(int seed) {
+      Seed = seed;
+    }
+
+    protected override List<List<double>> GenerateValues() {
+      var rand = new MersenneTwister((uint)Seed);
+
+      List<List<double>> data = new List<List<double>>();
+      var C_La = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
+      var a = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();
+      var C_Ld_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
+      var d_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();
+      var S_HT = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
+      var S_ref = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();
+
+      List<double> C_L = new List<double>();
+      data.Add(C_La);
+      data.Add(a);
+      data.Add(C_Ld_e);
+      data.Add(d_e);
+      data.Add(S_HT);
+      data.Add(S_ref);
+      data.Add(C_L);
+
+      double a0 = -2.0;
+
+      for (int i = 0; i < C_La.Count; i++) {
+        double C_Li = C_La[i] * (a[i] - a0) + C_Ld_e[i] * d_e[i] * S_HT[i] / S_ref[i];
+        C_L.Add(C_Li);
+      }
+
+      return data;
+    }
+  }
+}
Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftMaximumLift.cs
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftMaximumLift.cs	(revision 16495)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftMaximumLift.cs	(revision 16495)
@@ -0,0 +1,144 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class AircraftMaximumLift : ArtificialRegressionDataDescriptor {
+    public override string Name { get { return "Aircraft Maximum Lift Coefficient f(X) = x1 - 1/4 x4 x5 x6 (4 + 0.1 x2/x3 - x2²/x3²) + x13 x14/x15 x18 x7 - x13 x14/x15 x8 + x13 x14/x15 x9 + x16 x17/x15 x18 x10 - x16 x17/x15 x11 + x16 x17/x15 x12"; } }
+
+    public override string Description {
+      get {
+        return "Slightly changed version of the problem instance given in: " + Environment.NewLine +
+          "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " +
+          "modeling generalized separable systems\", " +
+          "pre-print on arXiv: https://arxiv.org/abs/1706.02281 ." + Environment.NewLine +
+          "Notably, this problem is missing from the peer-reviewed version of the article in Expert Systems with Applications, Volume 109" + Environment.NewLine +
+          "Function: f(X) = x1 - 0.25 x4 x5 x6 (4 + 0.1 x2/x3 - x2²/x3²) + x13 x14/x15 x18 x7 - x13 x14/x15 x8 + x13 x14/x15 x9 + x16 x17/x15 x18 x10 - x16 x17/x15 x11 + x16 x17/x15 x12" + Environment.NewLine +
+          "with x1 ∈ [0.4, 0.8]," + Environment.NewLine +
+          "x2 ∈ [3, 4]," + Environment.NewLine +
+          "x3 ∈ [20, 30]," + Environment.NewLine +
+          "x4, x13, x16 ∈ [2, 5]," + Environment.NewLine +
+          "x14, x17 ∈ [1, 1.5]," + Environment.NewLine +
+          "x15 ∈ [5, 7]," + Environment.NewLine +
+          "x18 ∈ [10, 20]," + Environment.NewLine +
+          "x8, x11 ∈ [1, 1.5]," + Environment.NewLine +
+          "x9, x12 ∈ [1, 2]," + Environment.NewLine +
+          "x7, x10 ∈ [0.5, 1.5]." + Environment.NewLine +
+          "Values for x5 and x6 have not been specified in the reference paper." +
+          " We therefore only use a single (x5) variable in place of ∆αW/c and set x6 to a constant value of 1.0." + Environment.NewLine +
+          "The range for x5 is [0..20].";
+      }
+    }
+
+    protected override string TargetVariable { get { return "f(X)"; } }
+    protected override string[] VariableNames { get { return new string[] { "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16", "x17", "x18", "f(X)" }; } }
+    protected override string[] AllowedInputVariables { get { return VariableNames.Except(new string[] { TargetVariable }).ToArray(); } }
+    protected override int TrainingPartitionStart { get { return 0; } }
+    protected override int TrainingPartitionEnd { get { return 100; } }
+    protected override int TestPartitionStart { get { return 100; } }
+    protected override int TestPartitionEnd { get { return 200; } }
+
+    public int Seed { get; private set; }
+
+    public AircraftMaximumLift() : this((int)System.DateTime.Now.Ticks) { }
+
+    public AircraftMaximumLift(int seed) {
+      Seed = seed;
+    }
+
+    protected override List<List<double>> GenerateValues() {
+      var rand = new MersenneTwister((uint)Seed);
+
+      List<List<double>> data = new List<List<double>>();
+      var x1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
+
+      var x2 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3.0, 4.0).ToList();
+
+      var x3 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 20.0, 30.0).ToList();
+
+      var x4 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
+      var x13 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
+      var x16 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
+
+
+      // in the reference paper \Delta alpha_w/c is replaced by two variables x5*x6. 
+      var x5 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 20).ToList(); // range for X5 is not specified in the paper, we use [0°..20°] for ∆αW/c
+      var x6 = Enumerable.Repeat(1.0, x5.Count).ToList(); // range for X6 is not specified in the paper. In the maximum lift formular there is only a single variable ∆αW/c in place of x5*x6.
+
+      var x7 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
+      var x10 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
+
+      var x8 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
+      var x11 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
+
+      var x9 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();
+      var x12 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();
+
+      var x14 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
+      var x17 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
+
+      var x15 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();
+
+      var x18 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 10.0, 20.0).ToList();
+
+
+      List<double> fx = new List<double>();
+      data.Add(x1);
+      data.Add(x2);
+      data.Add(x3);
+      data.Add(x4);
+      data.Add(x5);
+      data.Add(x6);
+      data.Add(x7);
+      data.Add(x8);
+      data.Add(x9);
+      data.Add(x10);
+      data.Add(x11);
+      data.Add(x12);
+      data.Add(x13);
+      data.Add(x14);
+      data.Add(x15);
+      data.Add(x16);
+      data.Add(x17);
+      data.Add(x18);
+      data.Add(fx);
+
+      for (int i = 0; i < x1.Count; i++) {
+        double fxi = x1[i];
+        fxi = fxi - 0.25 * x4[i] * x5[i] * x6[i] * (4 + 0.1 * (x2[i] / x3[i]) - (x2[i] / x3[i]) * (x2[i] / x3[i]));
+        fxi = fxi + x13[i] * (x14[i] / x15[i]) * x18[i] * x7[i];
+        fxi = fxi - x13[i] * (x14[i] / x15[i]) * x8[i];
+        fxi = fxi + x13[i] * (x14[i] / x15[i]) * x9[i];
+        fxi = fxi + x16[i] * (x17[i] / x15[i]) * x18[i] * x10[i];
+        fxi = fxi - x16[i] * (x17[i] / x15[i]) * x11[i];
+        fxi = fxi + x16[i] * (x17[i] / x15[i]) * x12[i];
+
+        fx.Add(fxi);
+      }
+
+      return data;
+    }
+  }
+}
Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/FluidDynamics.cs
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/FluidDynamics.cs	(revision 16495)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/FluidDynamics.cs	(revision 16495)
@@ -0,0 +1,90 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class FluidDynamics : ArtificialRegressionDataDescriptor {
+    public override string Name { get { return "Flow Psi = V_inf r sin(th) (1 - R²/r²) + G/(2 π) ln(r/R)"; } }
+
+    public override string Description {
+      get {
+        return "A full description of this problem instance is given in: " + Environment.NewLine +
+          "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " +
+          "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " +
+          "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine +
+          "Function: Psi = V_inf r sin(th) (1 - R²/r²) + G/(2 π) ln(r/R)" + Environment.NewLine +
+          "with V_inf ∈ [60 m/s, 65 m/s]," + Environment.NewLine +
+          "th ∈ [30°, 40°]," + Environment.NewLine +
+          "r ∈ [0.2m, 0.5m]," + Environment.NewLine +
+          "R ∈ [0.5m, 0.8m]," + Environment.NewLine +
+          "G ∈ [5 m²/s, 10 m²/s]";
+      }
+    }
+
+    protected override string TargetVariable { get { return "Psi"; } }
+    protected override string[] VariableNames { get { return new string[] { "V_inf", "th", "r", "R", "G", "Psi" }; } }
+    protected override string[] AllowedInputVariables { get { return new string[] { "V_inf", "th", "r", "R", "G" }; } }
+    protected override int TrainingPartitionStart { get { return 0; } }
+    protected override int TrainingPartitionEnd { get { return 100; } }
+    protected override int TestPartitionStart { get { return 100; } }
+    protected override int TestPartitionEnd { get { return 200; } }
+
+    public int Seed { get; private set; }
+
+    public FluidDynamics() : this((int)System.DateTime.Now.Ticks) { }
+
+    public FluidDynamics(int seed) {
+      Seed = seed;
+    }
+
+    protected override List<List<double>> GenerateValues() {
+      var rand = new MersenneTwister((uint)Seed);
+
+      List<List<double>> data = new List<List<double>>();
+      var V_inf = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 60.0, 65.0).ToList();
+      var th = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 30.0, 40.0).ToList();
+      var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.2, 0.5).ToList();
+      var R = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 0.8).ToList();
+      var G = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList();
+
+      List<double> Psi = new List<double>();
+      data.Add(V_inf);
+      data.Add(th);
+      data.Add(r);
+      data.Add(R);
+      data.Add(G);
+      data.Add(Psi);
+
+      for (int i = 0; i < V_inf.Count; i++) {
+        var th_rad = Math.PI * th[i] / 180.0;
+        double Psi_i = V_inf[i] * r[i] * Math.Sin(th_rad) * (1 - (R[i] * R[i]) / (r[i] * r[i])) +
+                     (G[i] / (2 * Math.PI)) * Math.Log(r[i] / R[i]);
+        Psi.Add(Psi_i);
+      }
+
+      return data;
+    }
+  }
+}
Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/PhysicsInstanceProvider.cs
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/PhysicsInstanceProvider.cs	(revision 16495)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/PhysicsInstanceProvider.cs	(revision 16495)
@@ -0,0 +1,57 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class PhysicsInstanceProvider : ArtificialRegressionInstanceProvider {
+    public override string Name { get { return "Physics Benchmark Problems"; } }
+    public override string Description { get { return ""; } }
+    public override Uri WebLink { get { return new Uri(@"https://doi.org/10.1016/j.eswa.2018.05.021"); } }
+    public override string ReferencePublication {
+      get {
+        return "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021 as well as the (slightly different) pre-print on arXiv: https://arxiv.org/abs/1706.02281";
+      }
+    }
+
+    public int Seed { get; private set; }
+
+    public PhysicsInstanceProvider() : this((int)DateTime.Now.Ticks) { }
+
+    public PhysicsInstanceProvider(int seed) : base() {
+      Seed = seed;
+    }
+
+    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
+      var rand = new FastRandom(Seed);
+
+      return new List<IDataDescriptor>()
+      {
+         new RocketFuelFlow(rand.Next()),
+         new AircraftLift(rand.Next()),
+         new FluidDynamics(rand.Next()),
+         new AircraftMaximumLift(rand.Next())
+      };
+    }
+  }
+}
Index: /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs
===================================================================
--- /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs	(revision 16495)
+++ /stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs	(revision 16495)
@@ -0,0 +1,86 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class RocketFuelFlow : ArtificialRegressionDataDescriptor {
+    public override string Name { get { return "Rocket Fuel Flow m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1)))"; } }
+
+    public override string Description {
+      get {
+        return "A full description of this problem instance is given in: " + Environment.NewLine +
+          "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " +
+          "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " +
+          "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine +
+          "Function: m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1)))" + Environment.NewLine +
+          "with p0 ∈ [4e5 Pa, 6e5 Pa]," + Environment.NewLine +
+          "A ∈ [0.5m², 1.5m²]," + Environment.NewLine +
+          "T0 ∈ [250°K, 260°K]," + Environment.NewLine +
+          "γ=1.4 and R=287 J/(kg*K)" + Environment.NewLine +
+          "The factor sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1))) is constant as γ and R are constants.";
+      }
+    }
+
+    protected override string TargetVariable { get { return "m_dot"; } }
+    protected override string[] VariableNames { get { return new string[] { "p0", "A", "T0", "m_dot" }; } }
+    protected override string[] AllowedInputVariables { get { return new string[] { "p0", "A", "T0" }; } }
+    protected override int TrainingPartitionStart { get { return 0; } }
+    protected override int TrainingPartitionEnd { get { return 100; } }
+    protected override int TestPartitionStart { get { return 100; } }
+    protected override int TestPartitionEnd { get { return 200; } }
+
+    public int Seed { get; private set; }
+
+    public RocketFuelFlow() : this((int)System.DateTime.Now.Ticks) { }
+
+    public RocketFuelFlow(int seed) {
+      Seed = seed;
+    }
+
+    protected override List<List<double>> GenerateValues() {
+      var rand = new MersenneTwister((uint)Seed);
+
+      List<List<double>> data = new List<List<double>>();
+      var p0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0e5, 6.0e5).ToList();
+      var A = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
+      var T0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList();
+
+      List<double> m_dot = new List<double>();
+      data.Add(p0);
+      data.Add(A);
+      data.Add(T0);
+      data.Add(m_dot);
+      double R = 287.0;
+      double γ = 1.4;
+      var c = Math.Sqrt(γ / R * Math.Pow(2 / (γ + 1), (γ + 1) / (γ - 1)));
+      for (int i = 0; i < p0.Count; i++) {
+        double m_dot_i = p0[i] * A[i] / Math.Sqrt(T0[i]) * c;
+        m_dot.Add(m_dot_i);
+      }
+
+      return data;
+    }
+  }
+}
